P-608 MAKING MOTIVATIONAL INTERVIEWING MORE ACCESSIBLE IN WORK REHABILITATION
Bibliographic record
Abstract
Abstract Introduction Motivational Interviewing (MI) is a person-centered communication approach. To promote its implementation, a feasible and plausible adaptation for work rehabilitation is needed. Objective To adapt a MI intervention for work rehabilitation. Method A consensus approach was used with 9 clinician experts (occupational therapists, physiotherapists, psychologists), including one MI expert psychologist. The expert clinicians had more than 2 years of experience in work rehabilitation for musculoskeletal disorders and more than 6 hours of MI training. Based on our review of the MI literature, a draft MI intervention outlined in a logic model was created and submitted to the experts, along with a questionnaire to assess agreement on the feasibility and plausibility of the logic model. Where there was disagreement, improvements were suggested. Subsequently, group meetings with the experts helped to reach consensus on the final version of the model. Results In total, 17 statements (51.5%) in the questionnaire reached consensus (agreement score ≥85%). The 16 statements that did not reach consensus (48.5%) generated 96 proposals (1 to 6 per statement) that were discussed in three 2-hour sessions. The experts agreed on the resources/tools required to carry out the 18 activities to achieve the five outcomes necessary to support workers’ self-determination in their work rehabilitation. Training recommendations were made to support implementation. Conclusions An operationalized MI intervention for work rehabilitation is now available for integration into existing programs. The next step will be to evaluate the usability of MI training in work rehabilitation.
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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 | 0.036 | 0.056 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".