pggppg

training in mining for reduction of lost-time injuries$

Kathleen M. Kowalski-Trakofler*, Edward A. Barrett

National Institute for Occupational Safety and Health (NIOSH), Pittsburgh Research Laboratory, 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA

Received 14 February 2003; accepted 27 May 2003

Abstract

Introduction: This paper discusses the application of a training intervention that uses degraded images for improving the hazard

recognition skills of miners. Method: NIOSH researchers, in an extensive literature review, identified fundamental psychological principles

on perception that may be employed to enhance the ability of miners to recognize and respond to hazards in their dangerous work

environment. Three studies were conducted to evaluate the effectiveness of the degraded image training intervention. A model of hazard

recognition was developed to guide the study. Results: In the first study, miners from Pennsylvania, West Virginia and Alabama, who were

taught with the aid of degraded images, scored significantly better on follow-up hazard recognition performance measures than those trained

using traditional instructional methodologies. The second and third studies investigated the effectiveness of the training intervention at two

mining companies. Data collected over a 3-year period showed that lost-time injuries at mines in Alabama and Illinois declined soon after the

training intervention was instituted. Impact on Industry: Further exploration of the hazard recognition model and the development of other

interventions based on the model could support the validity of the steps in the hazard recognition model.

D 2003 National Safety Council and Elsevier Science Ltd. All rights reserved.

Keywords: Hazard recognition; Mining; Training; Occupational safety; Perception

1. Introduction

Mining is one of the most dangerous occupations in the

world. From the beginning, the removal of materials from

the earth has resulted in heavy human losses. In the United

States, from 1880 to 1910, mine explosions and other

accidents claimed thousands of victims. The worst year in

U.S. coal mining history was 1907, when 3,242 deaths

occurred. Public concern about the toll of deaths, injuries,

and destruction in mine accidents led the U.S. Congress to

form the Bureau of Mines in 1910.

In the beginning of the twentieth century, the average

death toll per year from mining was 1,500 or more.

Throughout the century, this toll decreased to an average

during the 1990s of less than 100 (Fig. 1). Regardless,

mining, along with agriculture, construction, and transportation, continues to rank amongst the most dangerous of

industries in which to work (Fig. 2). Recently, the fatality

rate has ‘‘flat-lined’’ (Fig. 3) and as a result, researchers are

now seeking new interventions, some exploring administrative controls that address modifying human behavior (such

as hazard recognition training) as opposed to adding more

engineering controls.

With the continued success in the reduction of mining

accidents and fatalities, as well as the influence of budgetary

concerns, Congress eliminated the Bureau of Mines in 1996,

retaining two research laboratories and creating a Mining

Safety and Health Research Division under the National

Institute for Occupational Safety and Health (NIOSH).

NIOSH is part of the Centers for Disease Control within

the Department of Health and Human Services. The focus of

the Institute continues to be on the mission of providing a

safe and healthy work environment for U.S. miners through

research and prevention.

The three studies reported in this paper were conducted

and data collected from 1994 to 1998. The work began

0022-4375/$ - see front matter D 2003 National Safety Council and Elsevier Science Ltd. All rights reserved.

doi:10.1016/j.jsr.2003.05.004

$ A portion of this paper is contained in the proceedings of the

Twenty-fifth Annual Institute on Mining Health, Safety, and Research.

Blacksburg, VA 1995; in IC 9422, Information Circular, U.S. Department

of Interior, 1995; and The 23rd International Congress of Applied

Psychology Madrid, Spain 1994. This paper represents the first time the

three studies have been reported.

* Corresponding author. Tel.: +1-412-386-4531.

E-mail address: kkowalski@cdc.gov (K.M. Kowalski-Trakofler).
with a thorough search of the literature for psychological

principles that could be applied to the task of hazard

recognition. A number of concepts were identified and

published in IC 9422, 1995 Bureau of Mines Information

Circular, U.S. Department of Interior. This paper discusses

the development, application, and evaluation of an intervention based on one of the concepts, degraded images.

Degraded images are scenes that are viewed under less

than optimal conditions, such as inadequate illumination,

eccentric angles, haze, dust, or partially hidden. It concludes with a summary of follow-up training programs

subsequently completed, and conclusions/recommendations

for future consideration.

2. Background

In mining, as in most other production-related industries,

the safety of workers is dependent upon many interrelated

factors, one of which is the worker’s ability to recognize

hazards in the workplace. This ability is critical in mining

because the work environment is confined, inherently dangerous, and constantly changing due to the mining process.

Workers must be alert and continuously cognizant of their

surroundings, particularly the condition of the immediate

roof and ribs.

The information necessary to recognize conditions that are

precursors of danger is often available in the form of visual

0

500

1,000

1,500

2,000

2,500

3,000

3,500

1900190419081912191619201924192819321936194019441948

1952

19561960196419681972197619801984198819921996

2000

Year

Fatalities

Fig. 1. Coal fatalities 1900 – 2001 (Office workers included starting in 1973) (Source: MSHA).

Fig. 2. Occupational injury death rates per 100,000 workers by industry division and year United States 1983 – 1998 (Source: CDC; data for 1998 excludes

New York State).

ff fy () 
cues. However, even the ‘‘best looking’’ roof and rib conditions may ultimately deteriorate, sometimes dramatically. It

is therefore hypothesized that the safety of miners depends, in

large part, upon their individual capabilities for recognizing

hazards in a timely and accurate manner. If a hazard is not

recognized, response by the worker will become reactive

instead of proactive, and may result in serious injury.

Mine Safety Health Administration (MSHA; U.S. Department of Labor officials, who are responsible for mine law

enforcement maintains a database of accidents, injuries, and

fatalities. MSHA’s descriptive accident report data have

shown that ‘‘failure to perceive’’ a hazard consistently

appears as a contributing factor leading to both fatal and

non-fatal injuries. Thus, it can be argued that the ability of

miners to recognize hazards is an ongoing problem.

Safety training is mandated in the United States for

miners in 30 CFR Part 48 and Part 46. Hazard recognition

training is one of the content areas of the mandated training.

The objective of all mandated mine training is to positively

affect the health and safety of all workers. It is intended not

only to meet the letter-of-the-law, but to ensure that mine

work activities are performed in a safe manner.

Methods of mine safety training have not changed

appreciably for many years. An earlier U.S. Bureau of

Mines sponsored study concluded that mine trainers tend

to rely heavily on the same instructional materials year after

year, and although innovative teaching techniques such as

games or simulations are fairly common, by design they are

usually limited to the factual recall of safety information

(Cole et al., 1988; Kowalski et al., 2001).

3. Previous studies

Technical investigations of mine hazards and their effect

on worker safety in the production process permeate the

mine health and safety literature. However, there has been

limited research on training for the recognition of mine

hazards. In one of the few relevant studies, Blignaut (1979a)

investigated hazard recognition abilities among gold miners

in 1979 for the Chamber of Mines in South Africa. He

concluded that visual search performance depends significantly upon individual search skills. In subsequent work,

Blignaut (1979b) examined the ability of mine workers to

differentiate between safe and dangerous rock. In this study,

he confirmed that visual skills training significantly improved this ability. In the latter investigation, Blignaut found

that skills training consisting of subjecting trainees to

exercises in detection were more effective than training that

provided workers only with verbal information about specific hazards. His hazard detection exercises included the

use of stereoscopic (3-D) slides for depicting the ground

hazards found in gold mines.

Numerous other studies in the literature suggest that

perceptual judgments are susceptible to training. The most

extensive base of information that ties training with the

improvement of visual skills appears in military studies on

target detection (Farnsworth, Malone, & Sexton, 1952;

Jones, Freitag, & Collyer, 1974; Leibowitz, 1967). In

other visual search studies, performance of subjects’ visual

skills was shown to change both qualitatively and quantitatively with extended training (Schneider & Shiffrin,

1977; Shiffrin & Schneider, 1977). These researchers

applied a two-process theory of human information processing to detection, search, and attention phenomena. The

two-process theory includes automatic processing (not

demanding of attention) and controlled processing (demanding of attention). Schneider and Shiffrin demonstrated that the learning of these processes improves search

performance.

The need for classroom simulations of real conditions

has long been recognized among training practitioners.

Fig. 3. United States mining fatalities 1992 – 2001 (Source: MSHA, 2002).
Gibson (1969), for example, suggested there should be

training devices that would simulate particular dangers

while allowing subjects to act safely or unsafely. As a

general rule, providing perceptual experience in recognizing visual cues during hazard recognition training should

lead to a desired response in actual underground situations. This precept was investigated by the Bureau of

Mines in determining the feasibility of improving a

miner’s ability to recognize roof and rib conditions

(Barrett, Wiehagen, & Peters, 1989; Barrett & Kowalski,

1995).

In the above simulation, it was shown that stereoscopic

(3-D) slides are an effective instructional aid for illustrating

ground hazards in the mine environment. These slides are a

high-fidelity medium that realistically simulates underground conditions, and as such, are an excellent proxy for

miners to learn to recognize the characteristics of unstable

mine roof and rib. Additionally, the effectiveness of providing perceptual experience in recognizing visual cues during

training was investigated, as workers’ performance on a

hazard recognition task was measured in the workplace

using actual mine roof and rib conditions. It was concluded

in these studies that, indeed, perceptual skills of miners in

recognizing hazards could be improved in the classroom

through simulation.

When hazard recognition is discussed, several knowledge bases are implicated. Hazard recognition is viewed as

a complex task, with sensory, perceptual, and cognitive

components. Consequently, information from many sources is applicable. Research on transportation safety, military target acquisition, and operator vigilance in industrial

settings initially appear concerned with superficially dissimilar perceptual problems and environments. However,

the underlying perceptual processes addressed by investigators in these seemingly disparate areas are often analogous to those involved in underground hazard recognition.

For the purposes of these studies, researchers developed a

model of hazard recognition based on these various areas

of research. The model of hazard recognition in mining

also benefited from basic laboratory research into human

information processing and studies of the development of

expertise in complex tasks (Perdue, Kowalski, & Barrett,

1995).

In a study conducted by Bureau of Mines, researchers’

interviews with coal miners revealed that nearly two-thirds

of the miners had either been injured or startled by rockfall

at least once during a 12-month period (Peters & Wiehagen, 1987). The miners apparently did not perceive the

danger until it was too late to avoid it. Numerous Mine

Safety and Health Administration (MSHA) accident investigation reports note that the failure of miners to recognize

hazardous roof conditions has contributed to many accidents. In one example, undetected areas of loose rock in the

mine roof (‘‘rolling roof’’) fell and caused fatal injuries to a

worker. Another fatality occurred when an ‘‘undetected’’

horseback formation fell as a worker was scaling the mine

roof. The direct cause of these accidents was reportedly the

failure to detect the hazardous rock conditions at these

locations.

4. A model of hazard recognition1

Although many different ways of conceptualizing the

process of hazard recognition have been considered, a

comparatively simple model was developed for this study

(Fig. 4). The researchers posit that in order to respond

appropriately to danger from potential mine hazards, an

individual must perform the following:

1. Detection of sensory cues: Detect the presence of

potentially relevant sensory cues or features in the

environment. Often this takes the form of discriminating

‘‘figures’’ from the other stimuli serving as ‘‘ground.’’ In

terms of signal detection theory, the hazard ‘‘signal’’

must be discriminated from the extraneous ‘‘noise’’ of

mine operations. Factors that may influence the sensory

discrimination of hazard features include illumination

levels, apparent color and brightness contrast of the

features with the surrounding environment, ‘‘embeddedness’’ of the hazard, and acuity of the observer.

2. Attentional selection: Selectively attend to a subset of

those features either overtly or in an automatic fashion,

favoring them with further processing. The amounts of

sensory input that can be attended to will generally be

limited by the automaticity with which the observer is

functioning, the ability of the observer to time-share

cognitive resources between tasks, and the mental

workload experienced.

3. Recognition of the hazard: Correctly recognize the

pattern of features that identify a particular hazard.

Experience or training provides the information that cues

have been differentially associated with the presence of

hazards in the past and thus are ‘‘diagnostic’’ of that

hazard. In addition, the degree to which the often

degraded ‘‘real-world’’ image of the hazard is consistent

with the observer’s cognitive representation of the hazard

will influence the speed and accuracy of recognition.

4. Confirm or disconfirm the hazard: Test the initial

hypothesis of the recognition or denial of a hazard by

searching further for confirming or disconfirming

evidence, the search being guided by some mental

representation or schema of the nature of the hazard and

the general environment of the underground mine. Since

people seem generally reluctant to disconfirm any

tentative hypothesis they hold (Watson, 1968), the

efficacy of this stage will be determined by the ability

of the observer to avoid a simple ‘‘confirmatory bias.’’

1 Source: U.S. Bureau of Mines IC 9422, 1995. U.S. Department of

Interior.

f, f fy () 
5. Select appropriate response: Consider and select an

appropriate response to identified hazards. To a certain

extent this depends upon the subjective evaluation of risk

by the observer – simply because a potentially hazardous

condition has been identified does not mean that the

observer views it as a personal threat that compels

avoidance. Objective danger does not always elicit an

appropriate response if there is no personal, subjective

assessment of danger (Benda & Hoyos, 1983).

4.1. Mining application of the model

An example of the application of this model to mining

might be a situation where an underground shuttle car

operator in the process of transporting coal from the mining

face may be driving at high speed under varied lighting

conditions. The operator at some point may detect a presence

in the path of the shuttle, perhaps initially simply as a

glimmer of reflected light or the impression of movement

ahead. The operator may then focus more attention on the

stimulus, either consciously or automatically attempting to

discern the nature of the object: What is its shape? its size? its

speed?

The initially ambiguous sensory impressions (a faint

outline, a gleam of light) may be interpreted as the presence

of reflective tape or a swaying cap light on a human form,

and thus the mental picture emerges of a miner walking

across the path of the shuttle car. The operator has then

recognized that these cues signal a potentially hazardous

event: a collision with a worker. Presumably the operator

will continue to attend to the hypothesized ‘‘worker’’

ahead, testing his or her interpretation by searching for

more confirming (or disconfirming) details. Once the operator has achieved a necessary amount of confidence in

the interpretation of a hazard, there are a variety of

responses to consider, depending on the proximity of the

miner, the miner’s behavior, and the braking capabilities of

the shuttle. Of course, all of these ‘‘stages’’ in responding

to a hazard are likely to be accomplished swiftly and

perhaps with little conscious awareness of discrete steps

in the process.

5. Training with degraded visuals

As noted earlier, it has been shown in Bureau studies that

hazard recognition skills of miners can be improved through

training conducted in the safety of the classroom. This

finding was followed by an extensive literature search of

fundamental psychological principles on perception. Concepts were identified and an innovative hazard recognition

training program for miners was adapted from techniques

used to train military observers in the identification of

camouflaged targets (Perdue et al., 1995; Kowalski, Fotta,

& Barrett, 1995).

In general, underground mining environments could be

said to offer the miner who is attempting to recognize

possible hazards a selection of consistently degraded visual

targets, in that identifying features may be poorly illuminated, minimally distinguished by contour or contrast,

masked from direct view, or embedded within a confusing

array of competing features. In Fig. 5, the features present in

this degraded scene are typical of degraded visual environments in underground mines. In fact, it is unusual to find an

environment underground that is not visually degraded.

5.1. Advantages of degraded training

Laboratory research suggests that visual degradation of a

stimulus hinders its correct identification by slowing both

the initial stimulus encoding process and the search for that

stimulus in memory (Sternberg, 1986). The perceiver finds

it more difficult (and takes a longer time) to construct an

intact or ‘‘cleaned up’’ mental representation of the target

stimulus in the first place. In addition, when subsequently

the mental representation of the stimulus is compared with

the contents of memory for identification, each comparison

operation takes longer to carry out because of the degradation still present in the mental representation. Thus, training

that cultivates degraded mental representations and creates

content for memory identification could shorten the time

necessary to identify and thus react to hazards.

Fig. 5. A degraded scene in underground coal mine.

Fig. 4. A Model of Hazard Recognition (Source: NIOSH, 1995).
Another explanation for the advantage of training with

‘‘degraded’’ targets may be attributed to the variability of

the training stimuli. To facilitate information processing,

individuals often store mental representations of typical

targets or situations, often referred to as ‘‘prototypes.’’

These mental prototypes are often arrived at by abstracting

the defining or most typical features of the targets (Franks &

Bransford, 1971; Posner & Keele, 1968). Generally, the

observer has a variety of related experiences that are

somehow ‘‘averaged’’ into a prototypical experience, or

what may be thought of as the best composite example of

those experiences. Thus, training for hazard recognition

using visually degraded stimuli will present a more representative sample of the natural variation in appearance likely

to be observed in the actual environment. The observer will

tend to abstract or construct a more useful representative

prototype that can be used to facilitate the identification of

new stimuli.

It could be expected that in training miners to recognize

hazards and dangerous situations in their highly variable

and often visually degraded environment, it would be

desirable to give them experience with equally variable

representations of these hazards in the training presentations. Thus, practice with a series of normally distributed

variations on common hazards should stimulate trainees to

actively generate their own prototypical mental models of

these hazards. This would tend to be useful as their

expertise develops and they become proficient at matching

these mental models to new conditions in the mine. On the

other hand, exposing trainees to a limited set of professional

well-lighted photographs or slides of hazards taken at close

range would unlikely provide them with a sense of the

variation they are likely to see on the job, would limit their

ability to abstract a usefully complete prototype of the

hazard, and would deprive them of the realistic contextual

and background features that facilitate real-world hazard

identification.

With these principles in mind, NIOSH researchers tested

the hypothesis that miners trained using degraded training

materials (slides) would be more proficient at identifying

hazards than miners trained using more traditional or

highlighted training materials. Examples of a highlighted

slide and a degraded slide are shown in Fig. 6 (highlighted)

and Fig. 7 (degraded).

Fig. 6 shows a miner’s foot positioned within a trailing

cable loop on the mine floor. This type of highlighted scene

is traditionally used by trainers to explain the potential

dangers of tripping or the sudden response of a cable to

tension from the take-up reel. Fig. 7 is a degraded scene

showing several potential hazards. The most obvious one

(highlighted in Fig. 6) is the miner’s foot in the cable loop.

More subtle, but equally important, potential hazards in Fig.

7 include working in a confined area between equipment

and rib, working without gloves and safety glasses, and

placing tools on equipment where they could be knocked off

and injure a worker.

5.2. Military observations

The natural visual degradation of important features in

underground mines would appear to have implications for

the training of miners. Military research on target identification suggests that if eventual target stimuli are to be

searched for and identified under degraded conditions,

observers should be trained to recognize them using training

stimuli that are similarly degraded. Cockrell (1979) conducted an experiment and confirmed that observers attempting to recognize targets in naturalistic settings must get

accustomed to seeing these targets under less than optimal

(degraded) conditions such as the target being partially

hidden from view, observed from an eccentric angle, viewed

through haze or dust, or inadequately illuminated. In his

experiment, observers were asked to correctly identify slides

of model cars that had been degraded to some extent by

obscuring significant portions of the cars. Observers who

had also been trained using degraded views produced the

most consistently accurate identifications. Observers trained

using clear, complete photographs of the cars suffered a

reduced ability in their identification performance when

confronted with the partially obscured stimuli in testing.

Fig. 6. Highlighted image.

Fig. 7. Degraded image.

f, f fy () 
Thus, the author concluded that training that specifically

camouflaged or degraded stimuli proved to be advantageous

in accuracy of later identification.

Cockell’s study suggests that training stimuli should be

similarly masked, embedded in a cluttered background, or

presented in highly variant forms. During the extensive

literature review, it was noted that the military had discovered that flight observers who were trained with less than

ideal pictures (degraded) as compared to ideal pictures

(highlighted) of their targets had a higher score on a

subsequent target identification exercise. Degraded pictures

refer to pictures where the target was partially hidden by

cloud cover, rain, or camouflage. Traditional, or highlighted

pictures refer to pictures where the hazard is clearly visible.

Individuals trained using degraded pictures increased their

skill level, resulting in greater success in identifying and

accurately striking a target.

5.3. Clutter

Another concept with direct application to hazard recognition in mining and related to the degraded concept is

clutter. Many of the geologic features indicative of roof

instability may be harder to detect because of the high

degree of clutter in the background against which they are to

be detected. In the visual search literature, the term ‘‘clutter’’ is usually employed to denote the presence of multiple,

superficially similar stimuli in proximity to the target

(Williams & Borow, 1963). Clutter provides a ‘‘noisy’’

background in which a target is difficult to detect. An

analogous clutter can be seen in the complexity or variability of the rock surfaces and geological features found in an

underground mine. For example, the very dangerous slab of

loose roof rock (entry top, right side) shown in Fig. 7 may

not be apparent to a miner, at least not on the most important

first look, because of significant clutter existing in the entry.

Clutter in this scene consists of soft clay bottom, water, and

loose top. In the language of signal detection theory, clutter

may be thought of as the ‘‘noise’’ from which the perceiver

must isolate the potential hazard, the ‘‘signal.’’ Often the

crucial features that are diagnostic of the presence of roof

instability are partially masked by the superposition of mine

equipment, the irregularity of the rock face, poor illumination, or other elements obstructing the line-of-sight of the

observer.

6. Degraded hazards investigations

6.1. Study A

Control (highlighted hazards) and experimental (degraded hazards) training modules were developed for use in

mandated Part 48 underground coal annual refresher training

class. Slides were selected for the modules based upon

whether the targeted hazard was conspicuous or whether it

was visually degraded in some way. Eighty-two mine workers were recruited from 10 separate annual refresher training

classes in cooperation with mining companies in three states:

Pennsylvania, West Virginia, and Alabama. Four of the

classes consisted of miners working in track (haulage) mines

in Pennsylvania, another four classes from belt mines in

Alabama, and the remaining two classes of miners from

various belt mines across West Virginia. Participants were

requested to report their age, the number of years of

experience they had in mining, and their current job classification. The first two columns in Table 1 present a breakdown of the experimental and control group subjects with

regard to: (a) their distribution across the three states; (b) the

type of mine in which they work; (c) their job classifications;

(d) the reported ages of the miners; and (e) their years of

experience in mining. The third column in Table 1 summarizes this same information for the overall sample.

The number of miners recruited from each class varied

from a minimum of five persons to a maximum of 12.

Although the classroom instruction was considered part of

their annual refresher training and all attendees received this

instruction, participation in the post-instructional test phase

of the study was voluntary and several miners declined

participation. Assignment of subjects to either the control

(highlighted) group or the experimental (degraded) group

was accomplished by alternately designating an entire class

of subjects at each site to receive instruction using either the

Table 1

Frequencies and descriptive statistics of demographic information for

experimental group, control group, and overall sample

Description Experimental

group

Control

group

Overall

sample

State of mine location:

PA Track mines 16(40%) 16(38%) 32(39%)

WV Belt mines 12(30%) 9(21%) 21(26%)

AL Belt mines 12(30%) 17(41%) 29(35%)

Total 40(100%) 42(100%) 82(100%)

Job classification:

Production:

Longwall 3(7.5%) 3(7%) 6(7%)

Continuous Miner 6(15%) 14(33%) 20(25%)

Management 3(7.5%) 0 3(4%)

Subtotal 12(30%) 17(40%) 29(36%)

Support:

Maintenance 7(17.5%) 7(17%) 14(17%)

General 18(45%) 16(38%) 34(41%)

Management 3(7.5%) 2(5%) 5(6%)

Subtotal 28(70%) 25(60%) 53(64%)

Total 40(100%) 42(100%) 82(100%)

Age of workers:

Mean 41.1 40.2 40.6

Std. Dev 6.56 7.35 6.94

Years Mining Experience:

Mean 17.1 15.8 16.4

Std. Dev 4.46 5.61 5.10

f, f fy () 
traditional slides of highlighted hazards (control group) or

the degraded hazard views (experimental group).

In both testing conditions, 11 instructional slides (depicting a combined total of 35 hazards) were displayed to the

class for five seconds per slide, after which a Mining

Industry Specialist reviewed the slides and discussed the

multiple hazards depicted in each. The procedure for the

training phase was similar for both the control and experimental groups, except for the manner in which hazards

were pictured in the training slides.

After the training phase, participants in both control and

experimental groups were tested individually on an assessment task that presented them with a sequence of 10 slides

illustrating a total of 36 known hazards. Subjects examined

each test slide for five seconds and then reported any

hazards identified to the experimenter. Scores were computed by tallying the number of correctly identified unsafe

conditions. The highest possible score a participant could

receive by identifying each hazard as well as recognizing

the one slide depicting no hazardous condition was 37.

Subsequent to the study, an item review, conducted as part

of the overall analysis, revealed a possible bias on the

assessment task with regard to three items, all of which

depicted a miner not wearing safety glasses. As part of the

degraded training, the experimental group had observed two

instances of miners not wearing safety glasses while the

control group observed no such hazard as part of the

highlighted instruction. This difference may have given

the experimental group an unfair advantage in performing

the tasks, and consequently these three items were deleted

from the assessment task, reducing the highest possible

score to 34.

6.1.1. Results for study A

The results indicate that scores on the assessment task for

the experimental group were statistically significantly higher

than scores for the control group. Means and standard

deviations were computed on the scores for the experimental (degraded) and control (highlighted) groups. Differences

between the means were analyzed using the independent

sample’s t test. Since the expectation was that the experimental group would obtain a higher score than the control

group, a one-tailed t test was performed. The computed

statistics are reported in Table 2.

A summary of the average scores obtained on the task by

demographic subgroupings of miners within the overall

sample is provided in Table 3. These subgroupings represent

a breakdown of the overall sample relative to the state in

which the miners worked and their job classification as listed

previously in Table 1. The first two columns of Table 3 list

the mean scores for the subgroups within the experimental

and control groups, respectively. The third column lists the

mean scores for the subgroups within the overall sample.

Comparing the means between the two groups, the average

scores for the experimental group exceed those obtained by

the control group in all but two of the subgroupings.

Note that a comparison for the production managers could

not be made since there were no production managers

assigned to the control group. However, for miners classified

by job under Longwall Production and under Continuous

Miner Production, the average scores were higher in the

control group than in the experimental group, suggesting that

the effectiveness of the degraded training varies across job

categories. Although the study was not designed to examine

differences across job categories, a closer scrutiny of the data

offers some explanation for the observed discrepancies in the

outcome. In the case of one set of production miners, given

the small number of persons (n = 3) within each of the

groups, a meaningful interpretation of any computed difference in the means is not realistic.

The intent of providing the information listed in Table 3

is to address the concern that the experimental group was

predisposed to performing better than the control group by

initial differences between the groups in their ability to

recognize hazards rather than by differences in the type of

instruction provided. Although time constraints prevented

researchers from pretesting participants on their hazard

recognition abilities, the demographic information obtained

is useful in determining whether miners were more or less

evenly distributed across the two groups on relevant demographic variables. As mentioned previously, there were no

production managers in the control group, and as may be

seen in Table 3, the three production managers assigned to

the experimental group had the highest average score (20.3)

Table 2

Means, standard deviations (SD), and results of t test for examining

differences in performance scores between the control and experimental

groups

Group Instructional

method

Number of

miners

Mean

score

SD t-value

Control Highlighted 42 11.8 4.0 2.57*

Experimental Degraded 40 14.0 3.8

* p < .01.

Table 3

Score means and standard deviations (in parentheses) by demographic

groupings of the experimental group, control group, and overall sample

Description Experimental

group

Control

group

Overall

sample

State of mine location

PA Track mines 16.5 (3.3) 13.3 (3.0) 14.9 (3.5)

WV Belt mines 11.7 (3.4) 9.7 (4.1) 10.8 (3.7)

AL Belt mines 13.1 (3.2) 11.5 (4.3) 12.2 (3.9)

Job classification

Production 13.2 (5.3) 12.1 (4.7) 12.6 (4.9)

Longwall 11.0 (1.7) 14.3 (5.1) 12.7 (3.9)

Continuous Miner 11.5 (5.4) 11.6 (4.7) 11.6 (4.8)

Management 18.7 (4.0) – 18.7 (4.0)

Support 14.4 (3.1) 11.6 (3.5) 13.1 (3.3)

Maintenance 14.3 (3.7) 12.0 (2.7) 13.7 (3.7)

General 14.3 (3.1) 11.2 (3.4) 12.8 (3.6)

Management 15.3 (1.5) 13.5 (7.8) 14.6 (4.2)

522 K.M. KowalskiTrakofler, E.A. Barrett / Journal of Safety Research 34 (2003) 515525
of all the subgroupings. However, when the overall sample

was reduced by eliminating these three managers, the

resultant t-value (2.14) remained statistically significant

(df = 77, p < .05).

Additionally, eliminating management from the sample

also eliminated any advantage the experimental group may

have had in terms of the previously observed differences in

age and years of mining experience. Any other differences

in the distribution of miners with regard to state (and type of

mine haulage) or job classification appear to be negligible or

to favor the control group. The conclusion is therefore that

miners instructed using the degraded hazard approach were

able to identify more hazards on a hazard recognition task

than miners instructed by the traditional highlighted approach as a result of the type of instruction they received.

6.2. Study B

One of the subject companies from study A, located in

Alabama, worked with NIOSH researchers to develop a

Degraded Hazard Recognition program for its workers. The

goal was to look at lost-time injuries for one year, incorporate the degraded hazard recognition intervention program

the next year, followed by a no-intervention year (no

degraded hazard recognition intervention taught), then compare the three years data on lost time injuries - pre, during,

and post intervention. The program was incorporated into

the company’s Part 48 training program with more than

2,300 miners participating each year over the three-year

period — 1994 through 1996.

The subject company operated four underground coal

mines. Fig. 8 shows the monthly incident rate for all four

mines for the years 1994 through 1996. It can be seen from

the figure that the incident rate oscillates, but the peak values

of each subsequent oscillation are less than the previous

peak. It appears that there is a downward trend in incident

rates during the three-year period. The latter was confirmed

by computing a Spearman correlation coefficient to determine if there is an association between the incident rate and

time. The coefficient showed a moderate negative correlation

(r =  0.53) with a highly significant p-value (0.0009). A

Poisson regression model was then used to test for a trend

over time (Ely, Dawson, Lemke, & Rosenberg, 1997). The

slope coefficient h was calculated to be  0.015 with a pvalue of < 0.0001. Clearly, because h is negative and

statistically different from zero, there is indeed a significant

downward trend in incident rates from 1994 through 1996 at

the Study B mine.

The program was evaluated by the company to be a

success at the end of 1995, the intervention year, and the

company requested a continuation of the degraded methodology for 1996 because it was already convinced that the

method of training was a contributing factor in the reduction

of injuries. This decision interrupted the study as originally

designed but provided data to draw some conclusions. The

degraded training continued into the third year; thus, the

miners received two years of the degraded training. Table 4

shows the total accidents, total employee-hours, and average

incident rate for 1994 through 1996. The incident rate

decreased from 18.88 to 17.17 to 12.03. The drop in

incident rates was 9.06% from 1994 to 1995 and 29.94%

from 1995 to 1996 – a significant decrease after the training

Fig. 8. Study B - Alabama Mine monthly incident rate per 200,000 employee hours with trend line (Source: NIOSH, 2002).

Table 4

Study B - Alabama Mine total accident, total employee-hours, and incident

rate per 200,000 employee hours 1994 – 1996 (Source: NIOSH, 2002)

Year Total accidents Total employee-hours Incident rate

1994 366 3,876,560 18.88

1995 346 4,030,116 17.17

1996 273 4,420,960 12.03

K.M. KowalskiTrakofler, E.A. Barrett / Journal of Safety Research 34 (2003) 515525 523
intervention. It is also important to note that company

management at all levels became more involved in safety

at approximately the same time. In this case, it is not

possible to determine the separate effects of the training

and the management involvement.

One of the four mines did not show a significant

decrease in incident rates over the three-year period. For

1994 the incident rate was 16.1, for 1995 it was 17.0, and

for 1996, 15.8. The reasons for this occurring are not

known. It does mean that if this mine is not considered

in the analysis, the decrease in the incident rate over the 3

years and the slope of the time trend line would be even

more significant.

6.3. Study C

NIOSH researchers worked with an underground coal

mine in southern Illinois to develop a degraded program as a

supplemental safety program for 550 workers. Fig. 9 shows

the yearly incident rate for 1984 through 1996. The incident

rates increase, decrease, and increase again. Table 5 shows

the total accident, total employee-hours, and incident rate for

this mine for the years 1994 through 1996. Conclusions that

can be drawn from the Illinois mine data include the fact that

the 1996 incident rate is the lowest since the opening of the

mine in 1984. It represents a 27.10% drop from 1995 to

1996. The degraded program along with a flex-and-stretch

program (begun in 1994) is credited with a substantial

decrease in average incidence rates and the lowest lost-time

injury rate since the mine opened in 1984. This mine was

experiencing serious low morale problems for internal and

external reasons. Many mines in the area of the state were

closing and communication was poor, yet the injuries went

down. It is important to note that the safety incentive

program at this mine was consistent (a reward program)

throughout the last 4 years, which further documented the

positive results of the degraded hazard recognition program

in conjunction with the flex-and-stretch program.

7. Conclusions and recommendations

The perceptual skills of miners can be improved in the

classroom using degraded visuals. Further, this type of

hazard recognition training has transfer potential for improving miners’ ability to recognize hazards in underground coal,

and a potential for limestone and surface sand and gravel

operations. The initial study, Study A, showed an increase in

miners’ ability to recognize hazards. Studies B and C showed

that the incident rates dropped significantly after the intervention in Alabama (29.94%) and in Illinois (27.10%). In

each case, there was another factor introduced by the

company at approximately the same time – management

involvement in Alabama and a flex-and-stretch program in

Illinois. This confounding of the results makes it impossible

to determine how much of the drop in incident rates was due

to the training itself. However, in interviews with the trainers, their observations credited the degraded hazard recognition program with a major part of the decrease in rates.

The authors recommend further study of the use of

degraded image training to improve hazard recognition

skills of workers at a variety of work sites and industries.

In addition, further exploration of the hazard recognition

model and the development of other interventions based on

Fig. 9. Study C - Illinois Mine average yearly incident rate per 200,000 employee hours 1984 – 1996 (Source: NIOSH, 2002).

Table 5

Study C - Illinois Mine total accident, total employee-hours, and incident

rate per 200,000 employee hours 1994 – 1996 (Source: NIOSH, 2002)

Year Total accidents Total employee-hours Incident rate

1994 63 1,158,380 10.88

1995 64 1,191,550 10.74

1996 52 1,328,780 7.83

ff fy () 
the model could support the validity of the steps in the

hazard recognition model.

8. Follow-up report on additional programs and

applications

In the past several years, mining industry and MSHA

training personnel have developed numerous programs using

the degraded method. A training module including a video,

slides, overheads, and instructor’s manual was produced in

cooperation with the State of Illinois, Department of Natural

Resources, and Eastern Illinois Community Colleges. In

addition, the method was used to develop a hazard recognition program for underground limestone and surface sand

and gravel, and in a program focused on falls in construction.

The hazard recognition method has been presented to a

number of audiences, but this paper represents the first

publication of the combined data from the three studies.

The literature review was presented at the International

Applied Psychology Conference in July 1994 in Madrid,

Spain. The Degraded Method of training was presented to

participants at the National Mine Instructor’s Conference in

October 1994 and again in October 1995. Training programs developed for the mining industry based on the

degraded image concept are available at www.cdc.gov/

niosh/mining/training.

Acknowledgements

NIOSH: Lynn L. Rethi, David Garry, Barbara Fotta,

Roberta Calhoun, Linda J. McWilliams, Patricia J. Lenart;

Kerr McGee Coal Co.: Lloyd Putzek; Illinois Bureau of

Natural Resources: Art Rice, Gary Janello; Eastern Illinois

Community College: Howard Stern; Jim Walter Resources:

Powell Dabbs; Vulcan: Richard Seago; Richard Fowkes;

Ron Werry.

References

Barrett, E. A., & Kowalski, K. M. (1995). Effective Hazard Recognition

Training Using a Latent Image, Three-Dimensional Slide Simulation

Exercise. Washington, DC: US Bureau of Mines, Department of the

Interior, 36 pp, RI 9527.

Barrett, E. A., Wiehagen, W. J., & Peters, R. H. (1989). Application of

Stereoscopic (3-D) Slides to Roof and Rib Hazard Recognition Training. Bureau of Mines Information, 9210.

Benda, H. V., & Hoyos, C. G. (1983). Estimating hazards in traffic situations. Accident Analysis and Prevention, 15, 1 – 9.

Blignaut, C. J. H. (1979a). The perception of hazard: I. Hazard analysis and

the contribution of visual search to hazard perception. Ergonomics, 22,

991 – 999.

Blignaut, C. J. H. (1979b). The perception of hazard: II. The contribution of

signal detection to hazard perception. Ergonomics, 22, 1177 – 1183.

Cockrell, J. T. (1979). Effective training for target identification under

degraded conditions. U.S. Army Research Institute for the Behavioral

and Social SciencesTechnical Paper, vol. 358.

Cole, H. P., Mallet, L. G., Haley, J. V., Berger, P. K., Lacefield, W. E.,

Wasielewski, R. D., Lineberry, G. T., & Wala, A. M. (1988). Research

and Evaluation Methods for Measuring Nonroutine Mine Health and

Safety Skills; Vols 1 and 2; Contract No. HO348040, University of

Kentucky; Bureau of Mines OFR Vol 1 - 18(1)-89, PB 89-196646;

Vol 2 OFR 18(2)-89, PB 89-196653.

Ely, J. W., Dawson, J. D., Lemke, J. H., & Rosenberg, J. (1997). An

Introduction to Time – Trend analysis. Infection Control and Hospital

Epidemiology, 18, 264 – 267.

Farnsworth, D., Malone, F., & Sexton, M. (1952). Relative delectability of

hues in air – sea rescue. Journal of the Optical Society of America, 42,

289.

Franks, J. J., & Bransford, J. D. (1971). Abstraction of visual patterns.

Journal of Experimental Psychology, 90, 65 – 74.

Gibson, E. J. (1969). Principles of Perceptual Learning and Development

( pp. 163 – 193).

Jones, D. B., Freitag, M., & Collyer, S. C. (1974). Air-to-Ground Target

Acquisition Source Book: A Review of the Literature. Prepared for the

Office of Naval Research, NTIS AD-A015 079.

Kowalski, K. M., Fotta, B., & Barrett, E. A. (1995). Modifying Behavior to

Improve Miners’ Hazard Recognition Skills Through Training. Proceedings of the Twenty-Sixth Annual Institute on Mining Health, Safety

and Research. Blacksburg, VA: Virginia Polytechnic Institute.

Kowalski, K. M., Vaught, C., Brnich, M. J., Mallett, L., Reinke, D., Rethi,

L., & Wiehagen, W. (2001). The Evolving Mining Workforce: Training

Issues. 32nd Annual Institute on Mining Health, Safety, and Research

Conference Proceedings. University of Utah, Salt Lake City, Utah,

August 6 – 10.

Leibowitz, H. W. (1967). The human visual system and image interpretation. Institute for Defense Analyses Research Paper (p. P-319).

Perdue, C., Kowalski, K. M., & Barrett, E. A. (1995). Hazard Recognition

in Mining: A Psychological Perspective. IC 9422. Washington, DC:

United States Department of the Interior.

Peters, R. H., & Wiehagen, W. J. (1987). Human factors contributing to

groundfalls in underground coal mines: Worker’s views. Bureau of

Mines Information Circular, 9127, 24.

Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas.

Journal of Experimental Psychology, 77, 353 – 363.

Schneider, W., & Schiffrin, R. M. (1977). Controlled and automatic human

information processing: I. Detection, search and attention. Psychological Review, 84, 1 – 66.

Shiffrin, R. M., & Schneider, W. (1977). Controlled and Automatic Human

Information Processing: II Perceptual Learning, Automatic Attending,

and a General Theory. Psychological Review, 84 (2), 127 – 190.

Sternberg, S. (1986). Intelligence Applied: Understanding and Increasing

Your Intellectual Skills. Harcourt Brace Jovanovich.

Watson, P. C. (1968). On the failure to eliminate hypotheses – A second

look. In P. C. Watson, & P. N. Johnson-Laird (Eds.), Thinking and

reasoning. Baltimore, MD: Penguin Books.

Williams, J. R., & Borrow, M. S. (1963). The Effect of Rate and Direction of Display Movement Upon Visual Search. Human Factors, 5,

139 – 146.

Kathleen M. Kowalski-Trakofler, Ph.D. is a Research Psychologist at the

Pittsburgh Research Laboratory of the National Institute for Occupational

Safety and Health (NIOSH). Her research interests include the application

of the behavioral sciences to issues in worker safety and health, the

development of training programs for the nation’s miners, and traumatic

incident stress in disasters. She is a certified Mine Safety and Health

Administration (MSHA) instructor.

Edward A. Barrett is a Mining engineer with the National Institute for

Occupational Safety and Health (NIOSH). He has conducted mining

research in the areas of ground control, mine explosions, and training for

more than 35 years. He is a certified Mine Safety and Health Administration

(MSHA) instructor, a certified OSHA Outreach Trainer for both construction

and general industry, and a Certified Mine Safety Professional (C.M.S.P.).