Determinants and predictors of absenteeism and return-to-work in workers with shoulder disorders
Bibliographic record
Abstract
BACKGROUND: Shoulder disorders lead to substantial productivity loss and determinants and predictors of sick leave are poorly known. OBJECTIVE: To perform a systematic review on determinants and predictors of sick leave and delayed return-to-work (RTW) in workers with shoulder disorders. METHODS: A systematic literature search was conducted and we included studies on workers with shoulder disorders that contained information on determinants or predictors for sick leave or RTW, The risk of bias of included studies was evaluated with a validated tool. RESULTS: Eight studies were included and four had a high risk of bias. The only determinants that were found significantly associated with delayed RTW or sickness absence in more than one study were an atraumatic history, disease severity and previous sickness absence. A clinical prediction rule was developed to predict sick leave in one study and included the following predictors: a longer duration of sick leave prior to consultation, higher shoulder pain, strain/overuse in usual activities and psychological complaints. CONCLUSION: Several determinants or predictors were identified in the present review, but there is currently inconsistent evidence on the role of any determinants or predictors of work absence or delayed RTW for workers with a shoulder disorder. More methodologically sound studies are needed.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".