Management of Chronic Musculoskeletal Disorders in the Workplace from the Perspective of Older Employees: A Mixed Methods Research Study
Bibliographic record
Abstract
(1) Background: This mixed methods research (MMR) study explored older employees’ experiences of chronic musculoskeletal disorders (CMSDs) in relation to their employment, their perspectives on managing these conditions in the workplace and the strategies used to facilitate and maintain their roles and responsibilities. The services offered to them were also identified. (2) Methods: A mixed methods exploratory sequential design was implemented. In the first qualitative phase, 16 semi-structured interviews gathered in-depth information from older employees. The findings informed the development of an online questionnaire in the survey phase, which was administered to older employees (N = 107). Both sets of findings were then integrated using a narrative joint display. (3) Results: The phenomena of presenteeism and leaveism were important components of employees’ strategies for managing their condition. The integrated findings highlighted the roles of employers, managers and social support in encouraging disclosure and supporting the management of CMSDs. The results also emphasised how self-management and professional health services are crucial for sustaining employability. (4) Conclusions: Current challenges call for employers to identify effective ways to support the ageing workforce and invest in training opportunities for managers and collaborative opportunities with healthcare professionals and other stakeholders. A flexible, empathetic and resourceful work environment is optimal for supporting sustained employability for an ageing workforce.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".