2nd place, PREMUS best paper competition: implementing return-to-work interventions for workers with low-back pain – a conceptual framework to identify barriers and facilitators
Bibliographic record
Abstract
OBJECTIVES: Workplace-based return-to-work (RTW) interventions (programs) for workers with low-back pain are more effective than usual healthcare. Nevertheless, the implementation of such interventions usually encounters many barriers within healthcare systems, workplaces, and insurance systems. The aims of this study were to first construct a conceptual framework to identify barriers and facilitators before implementing RTW interventions and second validate this conceptual framework empirically. METHODS: We conducted a literature review to identify barriers and facilitators described in three domains: (i) diffusion of innovations; (ii) implementation of healthcare programs; and (iii) implementation of low-back pain clinical guidelines. A selection process was used to identify core dimensions. To validate this framework, we conducted a multiple case study with embedded levels of analysis in two regions of France. Data were collected through semi-structured interviews and focus groups with key participants. RESULTS: An initial framework was constructed with eight dimensions to be studied before implementation. This framework was eclectic, with different theoretical backgrounds. After the validation phase, some dimensions were modified, resulting in a revised conceptual framework that was theoretically and empirically grounded. CONCLUSIONS: This conceptual framework is an important contribution to the field of implementation science. It can be used in various settings to identify barriers and facilitators prior to implementing RTW interventions. In line with recommendations on knowledge transfer, this will enable evidence-based implementation strategies to be drawn up, improving intervention uptake and thus facilitating occupational disability prevention in low-back pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.011 |
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".