How do Employees with Chronic Musculoskeletal Disorders Experience the Management of Their Condition in the Workplace? A Metasynthesis
Bibliographic record
Abstract
This metasynthesis contributes to an understanding of the experiences, perceptions, and attitudes of employees on managing chronic musculoskeletal disorders (CMSDs) at work. Many studies in this field are concerned with prevention or return-to-work (RTW) programmes. However, the purpose of this review was to synthesise evidence that only focuses on the employees' management of their CMSDs at work. The SPIDER framework was used to structure the question "How do employees with CMSDs experience the management of their condition in the workplace"? The literature search focused on articles published between 2011 and 2021, and the search was conducted using the following databases: MEDLINE, SCOPUS, CINAHL, AMED, PsycINFO. The review identified nine articles that explored employees' experiences of managing CMSDs at work. Thematic synthesis was used to create analytic themes which provided a more in-depth discussion of these experiences. The identified themes were: 'employees actively seek ways to manage their conditions', 'influence of work environment on employees with CMSDs' and 'optimising the relationship between employees and managers. This metasynthesis suggests that the ability to negotiate workplace support and manage CMSDs at work is influenced by the cultural and social environment of the organisation. Effective communication, care and trust between the employee is needed. The review also illustrated the need for healthcare professionals to provide support to employees at work.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".