Modification and Initial Psychometric Evaluation of the Physical Health Attitude Scale for Use in the Canadian Mental Health and Addictions Context
Bibliographic record
Abstract
INTRODUCTION: The Physical Health Attitude Scale (PHASe) tool was developed to better understand mental health nurses' attitudes towards their involvement and confidence in physical health care. This tool has been used in the United Kingdom and Australia; however, it has not been used in Canada. AIM: This study aims to modify and provide an initial psychometric evaluation of the PHASe tool for use in a Canadian mental health and addictions context. METHODS: In Phase 1, clinical experts (n = 8) were consulted to provide feedback on the content and face validity of the PHASe tool. In Phase 2, the PHASe tool was piloted with nurses at a large urban mental health and addiction organization in Ontario, Canada (n = 77). RESULTS: In Phase 1, 4 items were added and 5 items were removed from the tool based on feedback provided by experts. In Phase 2, 12 poorly correlated items were removed. A two-factor solution was identified, with subscales "confidence" and "barriers and attitudes". DISCUSSION: Initial psychometric evaluation suggests that a revised 15-item version of the PHASe tool is valid and reliable in a Canadian mental health and addictions context; however, more testing is recommended in larger, more diverse samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".