Women’s accounts of help-seeking in early rheumatoid arthritis from symptom onset to diagnosis
Bibliographic record
Abstract
BACKGROUND: As interest in gender and health grows, the notion that women are more likely than men to consult doctors is increasingly undermined as more complex understandings of help seeking and gender emerge. While men's reluctance to seek help is associated with practices of masculinities, there has been less consideration of women's help-seeking practices. Rheumatoid arthritis (RA) is a chronic disease that predominantly affects women and requires prompt treatment but considerable patient-based delays persist along the care pathway. This paper examines women's accounts of help seeking in early RA from symptom onset to diagnosis. METHODS: We conducted in-depth interviews with 37 women with RA <12 months in Canada. Analysis was based on a constant comparison, thematic approach informed by narrative analysis. RESULTS: The women's accounts featured masculine practices associated with men's help-seeking. The women presented such behaviours as relational, e.g. rooted in family socialisation and a determination to maintain roles and 'normal' life. DISCUSSION: Our findings raise questions about how far notions of gender operate to differentiate men and women's help seeking and may indicate more similarities than differences. Recognising this has implications for policy and practice initiatives for both men and women.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".