[Disability management in the international context: a systematic review of reviews].
Bibliographic record
Abstract
SUMMARY: Background. Disability Management (DM) was born to improve workers' health and to optimize return to work for people with disabilities. Objectives. The objective of this study was to elaborate a review of reviews published in literature on the use of Disability Management in international contexts and the strategies used to facilitate the return to work for individuals with cronic disabilities or post injury. Methods. The present review was carried out by consulting the Pubmed and Scopus database from 1994 to January 2021, according to the PRISMA guidelines. Initially, the duplicates were removed. Then, the eligible studies were selected through a multistep approach (title evaluation, abstract and full-text). The systematic reviews were evaluated using the AMSTAR method, while for the narrative reviews the INSA scale was used. Results. The research produced 186 results. Following the removal of the duplicates and articles with no available or not pertinent full text, 51 reviews were included: 17 systematic and 34 narrative. The analyzed studies were related to the DM policies of the United States, Canada and UK. Ten topics emerged, the most frequent ones including: possible solutions to adopt in the event of workers with musculo-skeletal diseases (50% of the studies); legal matters regarding issues of mental health and stress (41.2%). The quality of the articles was generally high. Discussion. The systematic review showed that the research activity on DM is conducted mainly in the Anglo-Saxon world. This review can give some interesting insights for the full implementation of DM at the national level.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.020 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".