A logic model for a self-management program designed to help workers with persistent and disabling low back pain stay at work
Bibliographic record
Abstract
BACKGROUND: Workers with persistent disabling low back pain (LBP) often encounter difficulty staying at work. Self-management (SM) programs can offer interesting avenues to help workers stay at work. OBJECTIVE: To establish the plausibility of a logic model operationalizing a SM program designed to help workers with persistent disabling LBP stay at work. METHODS: We used a qualitative design. A preliminary version of the logic model was developed based on the literature and McLaughlin et al.'s framework for logic models. Clinicians in work rehabilitation completed an online survey on the plausibility of the logic model and proposed modifications, which were discussed in a focus group. Thematic analyses were performed. RESULTS: Participants (n = 11) found the model plausible, contingent upon a few modifications. They raised the importance of making more explicit the margin of maneuver or "job leeway" for a worker who is trying to stay at work and suggested emphasizing a capability approach. Enhancing the workers' perceived self-efficacy and communication skills were deemed essential tasks of the model. CONCLUSION: A plausible logic model for a SM program designed for workers with disabling LBP stay at work was developed. The next step will be to assess its acceptability with potential 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".