‘You want to show you’re a valuable employee’: A critical discourse analysis of multi-perspective portrayals of employed women with fibromyalgia
Bibliographic record
Abstract
Background Advice on fibromyalgia, a chronic illness primarily affecting women, often presents it as incompatible with work and rarely covers how to remain employed. Yet many women do. Objectives We aimed to understand how these women, their family members, and workmates portrayed employees with fibromyalgia, and how these portrayals helped women retain employment. Methods We interviewed 22 participants, comprising five triads and three dyads of people who knew each other. Using the methodology of critical discourse analysis, we analysed the interview data within and across the triads/dyads through coding, narrative summaries, and relational mapping. Results Participants reported stereotypes that employees with fibromyalgia are lazy, malingering, and less productive than healthy workers. Countering these assumptions, participants portrayed the women as normal, valuable employees who did not 'give in' to their illness. The portrayals drew on two discourses, normalcy and mind-controlling-the-body, and a related narrative, overcoming disability. We propose that participants' portrayals helped women manage their identities in competitive workplaces and thereby remain employed. Discussion Our findings augment the very sparse literature on employment with fibromyalgia. Using a new approach, critical discourse analysis, we expand on known job-retention strategies and add the perspectives of two key stakeholders: family members and workmates.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".