Women's experiences of developing musculoskeletal diseases: Employment challenges and policy recommendations
Bibliographic record
Abstract
PURPOSE: To answer three specific questions: (i) How do women experience the workplace after the onset of a musculoskeletal disease; (ii) What employment policy and programme suggestions can they offer for ways to better support chronically ill women in their abilities to maintain workforce participation; and (iii) How are these women's employment policy and programme recommendations informed by their own lived experiences and desires? METHOD: In-depth interviews were conducted with 18 women who had developed musculoskeletal diseases while involved in the labour market. Data were coded and analysed thematically. RESULTS: Participants identified three common workplace barriers experienced and three types of workplace accommodations commonly requested. They offered four specific employment policy and programme recommendations for ways to better support women who develop musculoskeletal diseases in maintaining labour market participation. It is found that their employment policy and programme recommendations are informed by their own experiences in the workplace and desires for being supported in maintaining involvement in paid labour. CONCLUSIONS: Creating employment programmes and policies that support chronically ill women in their attempts to remain involved in the workforce based on how much paid labour they are able to perform and where they are best able to work is of the utmost importance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".