Self-management programs to ensure sustainable return to work following long-term sick leave due to low back pain: A sequential qualitative study
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is a prevalent condition frequently leading to disability. Research suggests that self-management (SM) programs for chronic LBP should include strategies to promote sustainable return to work. OBJECTIVES: This study aimed to 1) validate and prioritize the essential content elements of a SM program in light of the needs of workplace representatives, and 2) identify the main facilitators and barriers to be considered when developing and implementing a SM program delivered via information and communication technologies (ICT). METHODS: A sequential qualitative design was used. We recruited workplace representatives and potential future users of SM programs (union representatives and employers) and collected data through focus groups and nominal group techniques to validate the relevance of the different elements included into 3 broad categories (Understand, Learn, Apply), as well as to highlight potential barriers and facilitators. RESULTS: Eleven participants took part in this study. The content elements proposed in the scientific literature for SM programs were found to align with potential future users' needs, with participants ranking the same elements as those proposed in the literature as the most important across all categories. Although some barriers were identified, workplace representatives believed that ICT offer an appropriate strategy for delivering individualized SM programs to injured workers who have returned to work. CONCLUSION: Our study suggests that the elements identified in the scientific literature as essential components of SM programs designed to ensure a sustainable return to work for people with LBP are in line with the needs of future users.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".