Special Issue Mental Health: occupational therapy practice, education, and research
Bibliographic record
Abstract
I am very pleased to introduce you to our Special Issue on Mental Health: Occupational Therapy Practice, Education, and Research. We had an excellent response from occupational therapists worldwide to the Editorial Board's call for articles for this issue. So I hope you enjoy reading this selection of papers, which highlight some of the diverse ways in which occupational therapists are contributing to research, education and practice in mental health care. Since we cannot showcase all the work submitted in the one issue, I hope this issue also whets your appetite for forthcoming contributions on mental health topics. It is also my special pleasure to introduce Dr Terry Krupa, Associate Professor and Chair of the Occupational Therapy Program at Queen's University, Canada, whom we invited to write a guest editorial for this special issue. She is a highly accomplished occupational therapist, educator and researcher, teaching in the areas of occupation and mental health and undertaking research focussed on the community lives and employment of people with mental health issues, as well as models of service delivery that promote recovery and citizenship. I hope you will enjoy reading her reflections on our responsibility as occupational therapists to champion occupational perspectives in mental health practice, education and research. Hopefully, you too will take away food for some thought-provoking dinners!
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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; both teacher heads agree on what is shown here.
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".