A Qualitative Investigation of the Barriers to Help-seeking Among Members of the Public Presented with Symptoms of New-onset Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Treating patients with rheumatoid arthritis (RA) within 3 months of symptom onset leads to significantly improved clinical outcomes. However, many people with RA symptoms wait a long time before seeking medical attention. To develop effective health interventions to encourage people to seek help early, it is important to understand what the general public knows about RA, how they would react to the symptoms of RA, and what might delay help-seeking. METHODS: Qualitative interviews were conducted with 38 members of the general public (32 women) without any form of inflammatory arthritis about their perceptions of RA symptoms and decisions to seek help were they to experience such symptoms. The interviews were audio-recorded, transcribed verbatim, and analyzed using thematic analysis. RESULTS: A number of barriers and drivers to help-seeking were identified and grouped into 5 themes: perceived causes of symptoms; factors related to presentation, location, and experience of symptoms; perceived effect of symptoms on daily life; self-management of symptoms; and general practitioner-related drivers and barriers. CONCLUSION: To our knowledge, our study is the first to investigate barriers to and drivers of help-seeking in response to the onset of RA symptoms in individuals without a diagnosis of RA. It has revealed a number of additional factors (e.g., the importance of the location of the symptoms) besides those previously identified in retrospective studies of patients with RA. Together with the data from previous research, these findings will help inform future health interventions aimed at increasing knowledge of RA and encouraging help-seeking.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".