Life stories of people with rheumatoid arthritis who retired early: how gender and other contextual factors shaped their everyday activities, including paid work
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to explore how contextual factors affect the everyday activities of women and men with rheumatoid arthritis (RA), as evident in their life stories. METHODS: Fifteen people with RA, who had retired early due to the disease, were interviewed up to three times, according to a narrative biographic interview style. The life stories of the participants, which were reconstructed from the biographical data and from the transcribed 'told story' were analysed from the perspective of contextual factors, including personal and environmental factors. The rigour and accuracy of the analysis were enhanced by reflexivity and peer-review of the results. RESULTS: The life stories of the participants in this study reflected how contextual factors (such as gender, the healthcare system, the support of families and social and cultural values) shaped their everyday activities. In a society such as in Austria, which is based on traditional patriarchal values, men were presented with difficulties in developing a non-paid-work-related role. For women, if paid work had to be given up, they were more likely to engage in alternative challenging activities which enabled them to develop reflective skills, which in turn contributed to a positive and enriching perspective on their life stories. Health professionals may thus use some of the women's strategies to help men. CONCLUSION: Interventions by health professionals in people with RA may benefit from an approach sensitive to personal and environmental factors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".