Identifying Occupational Performance Issues with Older Adults: Therapists’ Perspectives
Bibliographic record
Abstract
Background Identifying occupational performance issues is an essential component of the occupational therapy process. Little attention has been paid to therapists’ management of this aspect of geriatric practice. Purpose This study explored therapists’ approach to identifying occupational performance issues (OPI) with older adults. Methods Information gathered from semi-structured interviews was analyzed using Polkinghorne's (1995) analysis of narrative method. Findings The study demonstrated how therapists prepare clients to engage in the OPI identification process; use interviewing strategies to build trust; and tap into client narratives to foster hope in occupational possibilities. Implications Findings suggest that therapists require a complex set of highly skilled strategies to engage clients in OPI identification through tapping into aspects of the client's motivational influences, occupational histories, therapy expectations, and generational attitudes about aging. Further study is required to identify ways to overcome structural barriers to more occupational and narrative-based approaches to identifying occupational performance issues.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".