Examining utility and feasibility of implementing patient-reported outcome measures in occupational therapy primary care practice
Bibliographic record
Abstract
Introduction: Occupational therapists are increasingly part of interprofessional primary care teams, helping to expand primary care to meet client needs. Effectiveness of occupational therapy services is difficult to determine with currently collected data, and little is known about the best tools to use or how to integrate tools into practice. We explored the utility and feasibility of implementing patient-reported outcome measures (PROMs) within occupational therapy primary care practice. Method: We integrated pre-test/post-test and exploratory qualitative designs. Over 7 months, nine occupational therapists administered two PROMs to clients receiving falls prevention services, addressing falls efficacy and participation in daily occupations. Subsequent interviews with therapists explored the utility and feasibility of using the tools. We assessed pre-to-post change in PROM scores and thematically analyzed interview data. Results: The occupational therapists valued measuring function and participation in daily occupations to inform practice, communicate with team members, and demonstrate effectiveness. The falls efficacy scale appeared to capture change over time and was feasible to implement at pre-test. Conclusion: PROMs appear useful within occupational therapy primary care falls prevention services and can be feasible with attention to administration processes. Further development and testing of PROMs is needed to support occupational therapy primary care practice.
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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 | 0.169 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".