Evidence-informed decision about (de-)implementing return-to-work coordination – a case study
Bibliographic record
Abstract
Abstract BackgroundCoordination of return-to-work (RtW) with the aim to decrease sick leave in many countries. Compared to usual care, a Cochrane review found RtW coordination to have no considerable effect on sick leave. The aim of the study is to find out how the evidence from this review can be used for decisions about (de-)implementing RtW coordination in a country specific setting, using Finland as an example. MethodsWe conducted a systematic literature search and online survey with two groups of experts to collect data on the similarity of RtW interventions in Finland and those evaluated in the Cochrane review. Using content analysis methods we analysed how comparable the interventions in the Cochrane review are to Finish practice. We modelled the costs of RtW coordination compared to usual care for Finland. We used the criteria of the evidence-to-decision framework to draw conclusions about (de-)implementing RtW coordination in Finland. ResultsWe included 7 documents from the literature search and received survey data from 10 survey participants. RtW coordination included, both in Finland and in the review, at least one face-to-face meeting between the physician and the worker, a workers’ needs assessment, and an individual RtW plan and its implementation. Usual care focuses on medical treatment and may include general RtW advice. RtW coordination would be cost saving if it decreases sick leave with at least two days compared to usual care. The evidence in the Cochrane review was mainly of low certainty and the effect sizes had relatively wide confidence intervals. Only a new, high quality and large RCT can decrease the current uncertainty but this is unlikely to happen. The evidence-to-decision framework did not provide arguments for further implementation nor for de-implementation of the intervention. ConclusionsInterventions evaluated in the Cochrane review are similar to RtW coordination and usual care interventions in Finland. Considering all EtD framework criteria, including certainty of the evidence and costs, de-implementation of RtW coordination interventions in Finland seems unnecessary. Better evidence about the costs and stakeholders’ values regarding RtW coordination is needed to improve decision making.
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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 | 0.104 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".