Hand characteristics and functional abilities in predicting return to work in adult workers with traumatic hand injury
Bibliographic record
Abstract
BACKGROUND: Hand injuries affect a person's functioning, thus impeding their abilities to return to work. There is a limited understanding in return to work of the overall predictors when including hand characteristics and functional abilities. Therefore, it is essential to identify the most relevant predictors in return to work among individuals with a hand injury. OBJECTIVES: (1) To compare hand function characteristics and functional abilities of injured workers who have or have not returned to work. (2) To estimate hand function characteristics and functional abilities as predictors to return to work. METHODS: One hundred and fifteen adult workers with hand injuries aged 18- 59 years old from five general hospitals in Malaysia participated in a cross-sectional study. Predictors were estimated using logistic regression. RESULTS: There was a significant association between occupational sector (p = 0.012), injury duration (p = 0.024), occupational performance (p = 0.009) and satisfaction with performance (p < 0.001), grip strength of injured hand (p = 0.045- 0.002) and the Disability of Arm, Shoulder and Hand (DASH) disability/symptom (p = 0.001) with the person's return to work status. Significant predictors of return to work were identified using the Canadian Occupational Performance Measure (COPM) satisfaction's score, DASH disability/symptoms' score and duration of the injury. CONCLUSION: As two main predictors of return to work were COPM satisfaction and DASH disability/symptoms, occupational therapists working in rehabilitation should focus on achieving functional performance and satisfaction within the optimal time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".