title: Development of a mental health recovery module for the WHOQOL

authors: Rowthorn, Melissa J.

subjects: Quality of life

abstract: Purpose
The WHOQOL tools are widely used, multi-faceted, patient-rated, quality of life (QoL) measures, developed by the World Health Organization. The WHOQOL questionnaires are used to assess generic quality of life issues affected by all health problems. This study developed a module to use with the WHOQOL tools to improve their sensitivity to Health Related QoL issues relevant to mental health recovery.

Methods
Using a sequential mixed-methods approach, two research stages occurred. A qualitative stage invited 88 participants with experience of mental health recovery, into focus groups and importance rating activities, to identify candidate items for the new module. Following this, a quantitative stage involved 667 participants with, and without, mental health/addiction issues completing online or paper-based questionnaires to analyze which candidate items differentiated between those with and without mental health/addiction issues. Classical test theory and iterative Partial Credit Rasch Analysis were used to identify the most suitable candidate items for a reliable and valid mental health recovery module to be used with the WHOQOL tools.

Results
Seventeen candidate items captured important HRQoL facets relevant to mental health recovery. Rasch analysis removed 10 misfitting items. The final 7-item module, which demonstrated the best Rasch model fit, enquires about recovery beliefs, identifying strengths, self-awareness, acceptance, capacity to relate, feeling understood, and recovery progress. Ordinal-to-interval conversion tables have been developed to optimize measurement precision when using the module.

Conclusions
Important HRQoL issues central to mental health recovery can be reliably evaluated by using the recovery module with the WHOQOL tools.

text: Development of a mental health recovery module for the WHOQOL Purpose
The WHOQOL tools are widely used, multi-faceted, patient-rated, quality of life (QoL) measures, developed by the World Health Organization. The WHOQOL questionnaires are used to assess generic quality of life issues affected by all health problems. This study developed a module to use with the WHOQOL tools to improve their sensitivity to Health Related QoL issues relevant to mental health recovery.

Methods
Using a sequential mixed-methods approach, two research stages occurred. A qualitative stage invited 88 participants with experience of mental health recovery, into focus groups and importance rating activities, to identify candidate items for the new module. Following this, a quantitative stage involved 667 participants with, and without, mental health/addiction issues completing online or paper-based questionnaires to analyze which candidate items differentiated between those with and without mental health/addiction issues. Classical test theory and iterative Partial Credit Rasch Analysis were used to identify the most suitable candidate items for a reliable and valid mental health recovery module to be used with the WHOQOL tools.

Results
Seventeen candidate items captured important HRQoL facets relevant to mental health recovery. Rasch analysis removed 10 misfitting items. The final 7-item module, which demonstrated the best Rasch model fit, enquires about recovery beliefs, identifying strengths, self-awareness, acceptance, capacity to relate, feeling understood, and recovery progress. Ordinal-to-interval conversion tables have been developed to optimize measurement precision when using the module.

Conclusions
Important HRQoL issues central to mental health recovery can be reliably evaluated by using the recovery module with the WHOQOL tools.

year: 2019

title: Using model trees for classification

authors: Frank, Eibe

subjects: model trees

abstract: Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5’, based on Quinlan’s M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.

text: Using model trees for classification Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5’, based on Quinlan’s M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.

year: 1997-04

title: Ta moko: Maori tattoo

authors: Te Awekotuku, Ngahuia

subjects: Maori

abstract: The author examines the history, technique and meaning of ta moko (Maori tattoo) from prehistory to modern times.

text: Ta moko: Maori tattoo The author examines the history, technique and meaning of ta moko (Maori tattoo) from prehistory to modern times.

year: 1997