Measurement Properties of the Return-to-Work Self-Efficacy Scale in Workers with Shoulder Injuries
Bibliographic record
Abstract
Purpose: The objective of this study was to investigate the measurement properties of the Return-To-Work Self-Efficacy (RTWSE) scale in injured Canadian workers. Method: We assessed internal consistency, construct-convergent, and known-groups validity of the RTWSE scale’s total score and the pain management and re-injury self-efficacy (PRSE), supervisor support self-efficacy (SSE), and coworkers support self-efficacy (CWSE) domains in workers who had participated in a multidisciplinary rehabilitation program. Disability was measured by using the Disabilities of the Arm, Shoulder and Hand (DASH) scale. Spearman’s ρ, odds ratios, and the area under the receiver operating characteristic curve (AUC) were used to examine the strength of the associations. Results: The data of 57 injured workers (43 men [75%], mean age 52 [11] y) were used for analysis. Internal consistency of the domains was satisfactory, with Cronbach’s αs of 0.81, 0.87, and 0.92 for the CWSE, PRSE, and SSE, respectively. The PRSE domain correlated with the DASH ( r = 0.39) and relevant domains of the RTWSE scale ( rs = 0.47–0.78). The PRSE was able to differentiate between working and non-working people (AUC = 0.72). Satisfaction with the actual support received at work and overall job satisfaction correlated significantly with the total score and CWSE and SSE domains (AUCs ≥ 0.70). Conclusions: The RTWSE showed satisfactory internal consistency and construct convergent and known-groups validity in workers with shoulder injuries.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".