An occupational perspective in hand therapy: A scoping review
Bibliographic record
Abstract
Introduction There have been calls for the occupational therapy profession to realign with its values around occupation. However, the profession faces challenges in practice areas that have historically been underpinned by a biomechanical approach, such as hand therapy. The aim of this scoping review was to describe what is known about an occupational perspective in the hand therapy literature. Method This scoping review utilised a recognised five-step approach. A search was conducted in four databases for papers published between 2005 and 2016. Numerical and thematic analyses were employed, using the Canadian Practice Process Framework as a theoretical framework. Results Fifty papers were included in the scoping review. Six themes were identified: the centrality of occupation; inconsistencies in terminology; where in the occupational therapy process?; informal discussion as a key occupational strategy; occupational therapists’ expectations of patients; and a bottom-up approach focusing on biomechanical strategies to implementing the plan. Conclusion Whilst there has been some integration of an occupational perspective into hand therapy literature, there are still challenges. Inconsistent terminology, the lack of an occupational perspective throughout the occupational therapy process and a bottom-up approach to interventions all demonstrate the continuing tensions between the biomechanical approach and an occupational perspective.
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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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".