Designing a workplace return-to-work program for occupational low back pain: an intervention mapping approach
Bibliographic record
Abstract
BACKGROUND: Despite over 2 decades of research, the ability to prevent work-related low back pain (LBP) and disability remains elusive. Recent research suggests that interventions that are focused at the workplace and incorporate the principals of participatory ergonomics and return-to-work (RTW) coordination can improve RTW and reduce disability following a work-related back injury. Workplace interventions or programs to improve RTW are difficult to design and implement given the various individuals and environments involved, each with their own unique circumstances. Intervention mapping provides a framework for designing and implementing complex interventions or programs. The objective of this study is to design a best evidence RTW program for occupational LBP tailored to the Ontario setting using an intervention mapping approach. METHODS: We used a qualitative synthesis based on the intervention mapping methodology. Best evidence from systematic reviews, practice guidelines and key articles on the prognosis and management of LBP and improving RTW was combined with theoretical models for managing LBP and changing behaviour. This was then systematically operationalized into a RTW program using consensus among experts and stakeholders. The RTW Program was further refined following feedback from nine focus groups with various stakeholders. RESULTS: A detailed five step RTW program was developed. The key features of the program include; having trained personnel coordinate the RTW process, identifying and ranking barriers and solutions to RTW from the perspective of all important stakeholders, mediating practical solutions at the workplace and, empowering the injured worker in RTW decision-making. CONCLUSION: Intervention mapping provided a useful framework to develop a comprehensive RTW program tailored to the Ontario setting.
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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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".