Researcher perspectives on competencies of return-to-work coordinators
Bibliographic record
Abstract
PURPOSE: Return-to-work (RTW) coordination programs are successful in reducing long-term work disability, but research reports have not adequately described the role and competencies of the RTW coordinator. This study was conducted to clarify the impact of RTW coordinators, and competencies (knowledge, skills, and attitudes) required to achieve optimal RTW outcomes in injured workers. METHODS: Studies involving RTW coordination for injured workers were identified through literature review. Semi-structured interviews were conducted with 12 principal investigators to obtain detailed information about the RTW coordinator role and competencies not included in published articles. Interview results were synthesized into principal conceptual groups by affinity mapping. RESULTS: All investigators strongly endorsed the role of RTW coordinator as key to the program's success. Affinity mapping identified 10 groups of essential competencies: (1) individual traits/qualities, (2) relevant knowledge base, (3) RTW focus and attitude, (4) organizational/administrative skills, (5) assessment skills, (6) communication skills, (7) interpersonal relationship skills, (8) conflict resolution skills, (9) problem-solving skills, and (10) RTW facilitation skills. Specific consensus competencies were identified within each affinity group. Most investigators endorsed similar competencies, although there was some variation by setting or scope of RTW intervention. CONCLUSIONS: RTW coordinators are essential contributors in RTW facilitation programs. This study identified specific competencies required to achieve success. More emphasis on mentorship and observation will be required to develop and evaluate necessary skills in this area.
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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 | 0.037 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".