cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11421243
title: The Impact of Working Overtime or Multiple Jobs in Emergency Medical Services

description: <b>Background</b>: EMS professionals frequently work more than a standard 40-hour work week. For some EMS professionals, working overtime or multiple jobs may be necessary to make ends meet. Our objective was to evaluate the association between job satisfaction, intention to leave EMS, and financial dependence on overtime or working multiple jobs. <b>Methods</b>: We conducted a cross-sectional analysis of nationally-certified EMS professionals who completed an online questionnaire after submitting their recertification application (22,626/101,363, response rate = 22%) between October 2017 – May 2018. Items included dependence on additional work, satisfaction with job, and intentions of leaving the profession. Multivariable logistic regression models were used to describe the association between dependence on additional work, and job satisfaction and intention to leave EMS within 1 year and 5 years, controlling for age, sex, minority status, agency type, employment status, and urbanicity. <b>Results</b>: A total of 18,285 respondents were included in the analysis. The majority of EMS professionals (75%) worked over 40 hours per week. Most respondents (71%) were dependent on overtime or multiple jobs to make ends meet. Those who were dependent on extra work had higher odds of being dissatisfied with their job (aOR 1.92, 95% CI:1.64–2.44, and higher odds of intentions to leave EMS within 1 year and 5 years respectively (aOR 1.32, 95%CI:1.14–1.54; aOR 1.16, 95%CI:1.07–1.25). <b>Conclusion</b>: Many EMS professionals depend on additional work to make a living. Financial dependence was associated with increased odds of workforce-reducing factors, including job satisfaction and intention of leaving. This can lead to poor individual outcomes such as stress and burnout, as well as to turnover within the workforce, thus reducing the number of individuals available to provide high quality emergency care.
DOI: 10.6084/m9.figshare.11541765.v1
title: A Statewide Analysis of EMS’ Pediatric Transport Destination Decisions

description: <b>Introduction:</b> Deciding where to transport a patient is a key decision made by emergency medical services (EMS), particularly for children because pediatric hospital resources are regionalized. Since evidence-based guidelines for pediatric transport destinations are being developed, the purpose of this study was to use a large statewide EMS database to describe current patterns of EMS providers’ transport destination decisions for pediatric patients. <b>Methods:</b> This is a retrospective study of pediatric transports from 2011-2016 in EMS Tracking and Reporting System (EMSTARS), Florida’s statewide EMS database. We included patients greater than 1 day and less than or equal to 18 years who were primary EMS scene transports. Our primary outcome variable was ‘reason for choosing destination.’ We performed descriptive and comparative analysis between closest facility and all other ‘reason for choosing destination’ choices. We used geospatial analysis to examine destination choice in urban and rural counties. <b>Results:</b> Our final study sample was 446,274, and 48.2% of patients had closest facility as their ‘reason for choosing destination.’ The next largest category was patient/family choice (154,035 patients, 35.7%). Closest facility patients were older (median age 12 versus 10 years, p <b>Conclusions:</b> This large, statewide study describes EMS’ reason for choosing destination for pediatric patients. We found that just under half of patients were documented as closest facility, and over one-third as patient/family choice. Significant differences in destination reasons were noted for rural versus urban counties. This study can help those currently developing pediatric EMS destination guidelines by revealing a high proportion of patient/family choice and identifying conditions with high proportions of destination reasons other than closest facility.
DOI: 10.6084/m9.figshare.11560119
title: Success of Pediatric Intubations Performed by a Critical Care Transport Service

description: <b>Background:</b> Prehospital pediatric endotracheal intubation (ETI) is rarely performed. Previous research has suggested that pediatric prehospital ETI, when performed by ground advanced life support crews, is associated with poor outcomes. In this study, we aim to evaluate the first-attempt success rate, overall success rate and complications of pediatric prehospital ETI performed by critical care transport (CCT) personnel. <b>Methods:</b> We conducted a retrospective observational study in a multi-state CCT service performing rotor wing, ground, and fixed wing missions. We included pediatric patients (&lt;18 years) for whom ETI was performed by CCT personnel (flight nurse or flight paramedic). Our primary outcome of interest was rate of first-attempt ETI. Secondary outcomes were overall rates of successful ETI, complications encountered, and outcomes of patients with unsuccessful intubation. <b>Results:</b> 993 patients were included (63.2% male, median age 12 years, IQR 4–16 years). 807/993 (81.3%) patients were intubated on the first attempt. Lower rates of successful first-attempt intubation were seen in younger ages (42.9% in infants ≤30 days of age). In multivariable logistic regression, lower odds (adjusted odds ratio, 95% confidence interval) of successful first-attempt ETI were associated with ages &gt;30 days to &lt;1 year (0.33, 0.18-0.61) and 2 to &lt;6 years (0.60, 0.39–0.94) compared to patients 12 to &lt;18 years. Patients given an induction agent and neuromuscular blockade (NMB) had a higher odds of first-attempt ETI success (1.53, 1.06–2.15). 13 (1.3%) had immediately recognized esophageal intubation and 33 (3.3%) had vomiting. No episodes of pneumothorax were reported. 962/993 (96.9%) patients were successfully intubated after all attempts. In patients without successful ETI (n = 31), supraglottic airways were used in 24, bag-valve mask ventilation in 5, and surgical cricothyroidotomy in 2, with an overall advanced airway success rate of 988/993 (99.5%). <b>Conclusion</b>: Critical care flight nurses and paramedics performed successful intubations in pediatric patients at a high rate of success. Younger age was associated with lower success rates. Improved ETI training for younger patients and use of an induction agent and NMB may improve airway management in critically ill children.
DOI: 10.6084/m9.figshare.11560119.v1
title: Success of Pediatric Intubations Performed by a Critical Care Transport Service

description: <b>Background:</b> Prehospital pediatric endotracheal intubation (ETI) is rarely performed. Previous research has suggested that pediatric prehospital ETI, when performed by ground advanced life support crews, is associated with poor outcomes. In this study, we aim to evaluate the first-attempt success rate, overall success rate and complications of pediatric prehospital ETI performed by critical care transport (CCT) personnel. <b>Methods:</b> We conducted a retrospective observational study in a multi-state CCT service performing rotor wing, ground, and fixed wing missions. We included pediatric patients (&lt;18 years) for whom ETI was performed by CCT personnel (flight nurse or flight paramedic). Our primary outcome of interest was rate of first-attempt ETI. Secondary outcomes were overall rates of successful ETI, complications encountered, and outcomes of patients with unsuccessful intubation. <b>Results:</b> 993 patients were included (63.2% male, median age 12 years, IQR 4–16 years). 807/993 (81.3%) patients were intubated on the first attempt. Lower rates of successful first-attempt intubation were seen in younger ages (42.9% in infants ≤30 days of age). In multivariable logistic regression, lower odds (adjusted odds ratio, 95% confidence interval) of successful first-attempt ETI were associated with ages &gt;30 days to &lt;1 year (0.33, 0.18-0.61) and 2 to &lt;6 years (0.60, 0.39–0.94) compared to patients 12 to &lt;18 years. Patients given an induction agent and neuromuscular blockade (NMB) had a higher odds of first-attempt ETI success (1.53, 1.06–2.15). 13 (1.3%) had immediately recognized esophageal intubation and 33 (3.3%) had vomiting. No episodes of pneumothorax were reported. 962/993 (96.9%) patients were successfully intubated after all attempts. In patients without successful ETI (n = 31), supraglottic airways were used in 24, bag-valve mask ventilation in 5, and surgical cricothyroidotomy in 2, with an overall advanced airway success rate of 988/993 (99.5%). <b>Conclusion</b>: Critical care flight nurses and paramedics performed successful intubations in pediatric patients at a high rate of success. Younger age was associated with lower success rates. Improved ETI training for younger patients and use of an induction agent and NMB may improve airway management in critically ill children.
DOI: 10.7910/dvn/mkchln/s7jmml
title: Datasets.rar

description: :unav
DOI: 10.7910/dvn/mkchln/zmik9c
title: Data Dictionaries.rar

description: :unav
DOI: 10.7910/dvn/mkchln/cagv0e
title: Questionnaires.rar

description: :unav
DOI: 10.7910/dvn/mkchln/5p6xxm
title: Enumerators Training Manuals.rar

description: :unav
DOI: 10.6084/m9.figshare.5457451.v2
title: A new way to measure research/publication impact in Indonesian academic system

description: I drew this sketch-note during a meeting about measuring research/publication impact. This sketch-note clearly describe my thought about research process and how we should look at research output beyond the far-known metrics we are forced to use today.<br><br>My point is we already have quality controls (QC) starting from grant selection to critical assessment from readers. But still, we lay ourselves to widely used metrics that have been slowly fade away in the academic perspective. <br><br><b>My second point is</b> that indexing is far from measuring the research process. It only sets journal/conference standards not the whole research.<br><br><b>The third point is </b>by trusting those metrics and those alone as the objective clearly underestimates our own scientific background and logic that we have built throughout years of school era. <br><br>We should measure the impact of a research or paper using "open principles" through out its workflow, instead of only looking at the final product: <br><br>(1) is it original on certain level, is the data and analysis scientifically valid, <br>(2) is it reproducible and people can re-do the analysis<b>, </b>is it in shareable online in "ready to use" formats, does it provide data - code - method online,<br>(3) does it have value to community? Have the author engaged and fully interact with the community? <br><br>I hope you get my three points. If you still choose to use the metrics, would it be "the most naive" way to measure impact? I will share the slides and eventually the full paper later on.
DOI: 10.6084/m9.figshare.5457442
title: Datasets and R.code: "Sickness behaviors in vampire bats (Desmodus rotundus)"

description: Fileset contains datasets as well as R. code supporting "sickness behavior reduces allogrooming in vampire bats". R- code "dyadic_social_grooming_analysis" contains simple model of how sickness induced changes in allogrooming given to other individuals, could affect disease transmission.<br>Detailed description of individual datasets:<br>(1) "physiology_data" and R-code "physiology_analysis"group = Group the individual was housed in (1-7)experiment = Week, experiment 1 = week 1, experiment 2 = week 2individual = injected individualtreatment = treatment (LPS or PBS)bm.postmpre = bodymass post injection minus bodymass pre-injection (grams)WBC.postmpre = white blood cell concentration (cells/ml) post-injection minus pre-injectionNL.postmpre = NL ratio post-injection minus NL ratio pre-injection<br>(2) "individual_behaviors_means" and R-code "individual_behavior_analysis"focal.bat: focal bat of behavior scoring after injectiontreatment: treatment (LPS or PBS)activity: mean activity rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2sleep.rate: mean sleep rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2grooming.rate: mean self-grooming rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2ml.rate: mean mouthlicking rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2socgroom.rate: mean social grooming rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2moving.rate: mean moving rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2group: group the focal bat was housed in (from 1-7)groupsize: groupsize of the group the focal bat was housed in (2 or 4)<br>(3) "dyadic_social_grooming_dataset" and R-code "dyadic.social.grooming.analysis"focal.bat: focal bat of observationpartner: partner bat the focal bat was interacting withpair: description of two interacting bats as a pairgroup: group the focal bat was housed in (groups 1-7)groupsize: groupsize of the group the focal bat was housed in (2 or 4)day.in.exp: Day on the experimental timeline (see figure S1)week: Week in the experiment (week 1 or 2)duration.rec: duration in seconds the focal bat received grooming from partnerduration.act: duration in seconds the focal bat gave grooming to partnerhours.rec: hours recorded (always 2 h except for nights 5 and 13 due to technical problems)dir.dyad: unique description of a pair of two bats. Directional dyad, e.g focal bat is A and partner is B so directional dyad is B-&gt;Atreatment: treatment during the experimental week 1 or 2injection: injection = "no" for baseline nights and "yes" for nights when the focal bat was injected with either LPS or PBS
DOI: 10.6084/m9.figshare.5457421
title: Modeling Motor Learning Using Heteroscedastic Functional Principal Components Analysis

description: We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretable way to explore factors that affect the variability of functional data. Our work is motivated by a novel dataset from an experiment assessing upper extremity motor control, and quantifies the reduction in movement variability associated with skill learning. The proposed methods can be applied broadly to understand movement variability, in settings that include motor learning, impairment due to injury or disease, and recovery. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5457442.v2
title: Datasets and R.code: "Sickness behaviors in vampire bats (Desmodus rotundus)"

description: Fileset contains datasets as well as R. code supporting "sickness behavior reduces allogrooming in vampire bats". R- code "dyadic_social_grooming_analysis" contains simple model of how sickness induced changes in allogrooming given to other individuals, could affect disease transmission.<br>Detailed description of individual datasets:<br>(1) "physiology_data" and R-code "physiology_analysis"group = Group the individual was housed in (1-7)experiment = Week, experiment 1 = week 1, experiment 2 = week 2individual = injected individualtreatment = treatment (LPS or PBS)bm.postmpre = bodymass post injection minus bodymass pre-injection (grams)WBC.postmpre = white blood cell concentration (cells/ml) post-injection minus pre-injectionNL.postmpre = NL ratio post-injection minus NL ratio pre-injection<br>(2) "individual_behaviors_means" and R-code "individual_behavior_analysis"focal.bat: focal bat of behavior scoring after injectiontreatment: treatment (LPS or PBS)activity: mean activity rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2sleep.rate: mean sleep rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2grooming.rate: mean self-grooming rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2ml.rate: mean mouthlicking rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2socgroom.rate: mean social grooming rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2moving.rate: mean moving rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2group: group the focal bat was housed in (from 1-7)groupsize: groupsize of the group the focal bat was housed in (2 or 4)<br>(3) "dyadic_social_grooming_dataset" and R-code "dyadic.social.grooming.analysis"focal.bat: focal bat of observationpartner: partner bat the focal bat was interacting withpair: description of two interacting bats as a pairgroup: group the focal bat was housed in (groups 1-7)groupsize: groupsize of the group the focal bat was housed in (2 or 4)day.in.exp: Day on the experimental timeline (see figure S1)week: Week in the experiment (week 1 or 2)duration.rec: duration in seconds the focal bat received grooming from partnerduration.act: duration in seconds the focal bat gave grooming to partnerhours.rec: hours recorded (always 2 h except for nights 5 and 13 due to technical problems)dir.dyad: unique description of a pair of two bats. Directional dyad, e.g focal bat is A and partner is B so directional dyad is B-&gt;Atreatment: treatment during the experimental week 1 or 2injection: injection = "no" for baseline nights and "yes" for nights when the focal bat was injected with either LPS or PBS
DOI: 10.6084/m9.figshare.5457421.v2
title: Modeling Motor Learning Using Heteroscedastic Functional Principal Components Analysis

description: We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretable way to explore factors that affect the variability of functional data. Our work is motivated by a novel dataset from an experiment assessing upper extremity motor control, and quantifies the reduction in movement variability associated with skill learning. The proposed methods can be applied broadly to understand movement variability, in settings that include motor learning, impairment due to injury or disease, and recovery. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5457442.v3
title: Datasets and R.code: "Sickness behaviors in vampire bats (Desmodus rotundus)"

description: Fileset contains datasets as well as R. code supporting "sickness behavior reduces allogrooming in vampire bats". R- code "dyadic_social_grooming_analysis" contains simple model of how sickness induced changes in allogrooming given to other individuals, could affect disease transmission.<br>Detailed description of individual datasets:<br>(1) "physiology_data" and R-code "physiology_analysis"group = Group the individual was housed in (1-7)experiment = Week, experiment 1 = week 1, experiment 2 = week 2individual = injected individualtreatment = treatment (LPS or PBS)bm.postmpre = bodymass post injection minus bodymass pre-injection (grams)WBC.postmpre = white blood cell concentration (cells/ml) post-injection minus pre-injectionNL.postmpre = NL ratio post-injection minus NL ratio pre-injection<br>(2) "individual_behaviors_means" and R-code "individual_behavior_analysis"focal.bat: focal bat of behavior scoring after injectiontreatment: treatment (LPS or PBS)activity: mean activity rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2sleep.rate: mean sleep rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2grooming.rate: mean self-grooming rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2ml.rate: mean mouthlicking rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2socgroom.rate: mean social grooming rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2moving.rate: mean moving rate of 2 hours observation ((counts hour 1 / onscreen counts) + (counts hour 2 / onscreen counts)) / 2group: group the focal bat was housed in (from 1-7)groupsize: groupsize of the group the focal bat was housed in (2 or 4)<br>(3) "dyadic_social_grooming_dataset" and R-code "dyadic.social.grooming.analysis"focal.bat: focal bat of observationpartner: partner bat the focal bat was interacting withpair: description of two interacting bats as a pairgroup: group the focal bat was housed in (groups 1-7)groupsize: groupsize of the group the focal bat was housed in (2 or 4)day.in.exp: Day on the experimental timeline (see figure S1)week: Week in the experiment (week 1 or 2)duration.rec: duration in seconds the focal bat received grooming from partnerduration.act: duration in seconds the focal bat gave grooming to partnerhours.rec: hours recorded (always 2 h except for nights 5 and 13 due to technical problems)dir.dyad: unique description of a pair of two bats. Directional dyad, e.g focal bat is A and partner is B so directional dyad is B-&gt;Atreatment: treatment during the experimental week 1 or 2injection: injection = "no" for baseline nights and "yes" for nights when the focal bat was injected with either LPS or PBS
DOI: 10.6084/m9.figshare.5457421.v1
title: Modeling motor learning using heteroskedastic functional principal components analysis

description: We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretable way to explore factors that affect the variability of functional data. Our work is motivated by a novel dataset from an experiment assessing upper extremity motor control, and quantifies the reduction in motion variance associated with skill learning.