Using multiple stakeholders to define a successful return to work: A concept mapping approach
Bibliographic record
Abstract
OBJECTIVE: Currently no standard or universal outcome measure for return to work (RTW) programs exists making the evaluation and comparison of such programs difficult. RTW outcomes are often measured using nominal scales based on administrative data but these fail to take the perspectives of workers and other stakeholders into consideration. In order to gain that perspective this study was conducted to identify what outcomes are of interest and importance to RTW stakeholders. RTW stakeholders identified indicators of successful RTW in order to develop a conceptual framework of successful RTW. PARTICIPANTS: A total of 24 RTW stakeholders participated, representing both RTW consumers and providers from Southwestern Ontario. METHOD: This study used a mixed-method integrated form of concept mapping, which qualitatively generates and interprets data, and quantitatively analyzes data using multidimensional scaling and hierarchical cluster analysis. RESULTS: Participants generated 48 statements, which were subsequently clustered into the following six concepts; worker performance, worker job satisfaction, human rights, worker well-being, seamless RTW process through collaborative communication, and satisfaction of stakeholders other than workers. CONCLUSIONS: The results reflect the perspectives of stakeholders and suggest that RTW outcome measures are needed that not only evaluate all aspects of the worker's life, but the RTW process as well. Aside from confirming the inadequacy of nominal, administrative type outcomes, these findings imply that the actual RTW process is intimately tied to outcome. Implications and relevance are discussed for planning RTW programs and towards developing a RTW outcome tool.
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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 | 0.040 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".