Considerations for engaging in patient-oriented research with injured workers
Bibliographic record
Abstract
Background: Patient-oriented research (POR) incorporates patient-identified priorities and lived experiences into research. Despite their central role in return-to-work (RTW) planning, perspectives and priorities of injured workers are under-represented in Occupational Therapy research. Occupational therapists (OTs) play a key role in RTW research and practice, implementing evidence-based plans and patient-centered care, which positions them well to conduct POR. Purpose: The purpose of this paper is to identify considerations for POR approaches for OTs to engage injured workers in RTW research. Key issues: The engagement of injured workers as research partners is not well described or understood in POR. This paper outlines practical considerations for conducting POR with injured workers, addressing challenges such as power imbalances, communication barriers, fears of unemployment, and varying levels of vulnerability. OTs can facilitate knowledge transfer and act as knowledge brokers within the RTW process, leveraging their client-centered practice to lead research that optimally engages injured workers. Conclusion: Conducting POR with injured workers can shed light on their interactions with health, insurance, and compensation systems. POR approaches can highlight strengths and limitations of available services and systems and promote improved collaboration and knowledge translation and exchange. OTs can apply POR in research and practice to bridge this gap.
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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 | 0.435 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.024 | 0.033 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.007 | 0.034 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.015 | 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; the direct Gemma label and the distilled Codex classifier 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".