Women with Multiple Sclerosis and Employment Issues: A Focus on Social and Institutional Environments
Bibliographic record
Abstract
This paper examines employment issues for women diagnosed with multiple sclerosis (MS) and their workplace experiences, focusing analysis on the social and institutional dimensions of the environment. The analysis draws on data from a mixed method study using in-depth interviews and a survey. The findings indicate that although severity of symptoms affect employment status, non-medical factors, including modification of work conditions and understanding employers, and a supportive home environment with the possibility of delegating household tasks, can enhance women's ability to work. The specific focus in the paper on the experiences of women managing their disability in the workplace, from the qualitative phase of the study, acts as an analytic device to illustrate how context influences the way in which such factors play out. In high-lighting the issue of disclosure of diagnosis, and associated identity and income concerns for women, the paper demonstrates the importance of the social and institutional dimensions of environment in shaping occupational performance. The findings suggest that inclusion of environmental analysis in clinical practice broadens the range of intervention strategies to be considered and raises the issue of occupational therapists' role in advocacy.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".