Bibliographic record
Abstract
The occupational therapy profession, over the last 4 decades, has made many strides in unifying the language of the profession by developing and articulating theories that explain how use of occupation as intervention can influence health ( Lee et al., 2008 ; Lee, 2010 ). Occupational behavior models, also known as occupation-focused theories, emerged in the later decades of the 20th century as a tool to not only articulate the unique value of the profession but also provide a foundation for the professional identity of occupational therapy practitioners and a framework for everyday clinical reasoning ( Elliott et al., 2002 ; Ikiugu, 2010 ; Wimpenny et al., 2010 ). Examples of occupational behavior models generated include, but are not limited to, the Canadian Model of Occupational Performance and Engagement (CMOP-E), the Kawa Model, the Model of Human Occupation (MOHO), the Occupation Adaptation Model (OAM), the Person-Environment-Occupation (PEO) Model, and the Ecological Model ( Cole & Tufano, 2008 ; Schell & Gillen, 2018 ; Turpin & Iwama, 2011 ).
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".