Strengths-Based Interventions to Support Positive Role Identity in Home Health Practice
Bibliographic record
Abstract
The author collaborated with a home health occupational therapist in Western Washington. The therapist’s research question was, “What evidence is there to support strengths-based therapy interventions effective in supporting positive role identity in adults with physical disabilities who are receiving home health or outpatient rehabilitation services?” Home health practitioners may not account for a client’s mental health challenges that impact role identity. A client’s sense of role identity can influence re-engagement in meaningful activities that support quality of life. The evidence review found that role identity concepts, like autonomy, are considered to be important, but often measured as secondary outcomes.\nIn response to the occupational therapist’s interest in understanding her clients’ different psychosocial and emotional factors that facilitate continued engagement in meaningful occupations after discharge from occupational therapy (OT), the author presented an in-service on strengths-based interventions, followed by instruction in using the Canadian Occupational Performance Measure (COPM) to identify activities that clients personally value and to gauge their satisfaction with performance in such activities. To monitor the impact of the in-service and use of the COPM in practice, the therapist was interviewed before the in-service and after using the COPM for three weeks. She found using the COPM to be helpful in identifying goals that are meaningful to clients, but had limited amount of time with each client. She would like to continue to use the COPM in a non-standardized way to inform her evaluations and goal setting conversations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".