An Application of the Occupation Competence Model to Organizing Factors Associated with Return to Work
Bibliographic record
Abstract
The variations in return to work outcomes for ill or injured persons experiencing health leaves are complex. However, it is important to comprehend these variations in order to develop evidenced-based practice in work rehabilitation. Currently, a plethora of studies exist in the literature that have attempted to explain the variations in work outcomes. A 20-year review of the literature on work outcomes has revealed several limitations in using this knowledge in occupational therapy. The study of return to work outcomes is, for the most part, atheoretical and the knowledge base is fragmented and disorganized. In addition, the literature does not reflect a consistent understanding of the multidimensional nature of either work disability or the facilitators for return to work. In this paper, the Occupational Competence Model is presented as a framework for filling this gap. This model is used here to organize and synthesize the factors previously studied on work outcomes to foster an understanding of this literature from an occupational therapy perspective and the future study of work outcomes and work rehabilitation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".