Interdisciplinary team discussion on work environment issues related to low back disability: A multiple case study
Bibliographic record
Abstract
From both a social and cost-benefit perspective, disability prevention and associated management strategies for assisting employees with back pain are only successful if they address critical return-to-work (RTW) determining factors present in the workplace, including ergonomic factors. Despite our current knowledge of the importance of an integrated team approach targeting these factors, questions remain regarding the conceptual framework adopted by contemporary rehabilitation teams as it relates to work-oriented interventions to accommodate employees back to work. The purpose of this study was to explore and describe the work environment issues discussed by an interdisciplinary team engaged in the work rehabilitation process of low back disability cases. A qualitative content analysis was conducted for 10 sample cases that underwent a rehabilitation program in Quebec (Canada). Drawing on the results of these analyses, a framework representing a synopsis of practitioners' involvement in the person-environment dynamic is presented and discussed. This study makes a unique contribution to the understanding of the 'real-life' content of an interdisciplinary activity in disability management, thus, helping rehabilitation practitioners to build knowledge on the practical application of a cross-disciplinary framework to address work environment issues during the RTW process of those patients sick-listed due to back pain.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".