Impromptu everyday disclosure dances: how women with fibromyalgia respond to disclosure risks at work
Bibliographic record
Abstract
PURPOSE: Findings from a study examining how women with fibromyalgia remain employed are used to explicate a conceptualization that adds to literature on workplace disclosure of stigmatized illnesses and impairments: disclosure dances that employees improvise in response to workplace-relationships needs and disclosure risks. METHODS: Critical-discourse-analysis (CDA) methodology framed the study. Data were collected through 26 semi-structured, individual interviews with participant triads or dyads comprising women with fibromyalgia, family members and supervisors or co-workers. Interviews with managers who supervised disabled employees other than the women supplemented these data. Following coding, data were compared within and across triads/dyads through code-dimension summaries, narrative summaries and relational diagrams. RESULTS: Women with fibromyalgia and other stigmatized illnesses improvised everyday disclosures when they needed to explain fluctuating work ability, when others needed reminding about invisible impairments, and when workplace relationships changed. These impromptu disclosures comprised three dimensions: exposing oneself to scrutiny by disclosing both illness and impairments, divulging stigmatized illness, and revealing invisible impairments selectively. CONCLUSION: Through impromptu disclosure dances, women tailored disclosure to changing immediate circumstances. While assumptions from psychological theories of risk underlie current conceptualizations of disclosure as planned in advance, this article examines disclosure through a different lens: social theories of everyday risk. Implications for rehabilitation For women with fibromyalgia, disclosing illness and impairments at work may entail risks to their jobs and workplace relationships. Rehabilitation professionals need to consider these risks when advising women with fibromyalgia about disclosing their illness and impairments at work. Professionals may first want to learn from clients about their workplace cultures and relationships, and their perceptions of disclosure risk. Professionals can then suggest a range of disclosure responses, depending on the relationship and risk.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".