Everyday ethics and help-seeking in early rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Sociological understandings of chronic illness have revealed tensions and complexities around help-seeking. Although ethics underpins healthcare, its application in the area of chronic illness is limited. Here we apply an ethical framework to interview accounts and identify ethical challenges in the early rheumatoid arthritis (RA) experience. METHODS: In-depth interviews were conducted with eight participants who had been diagnosed with RA in the 12 months prior to recruitment. Applying the concepts of autonomous decision-making and procedural justice highlighted ethical concerns which arose throughout the help-seeking process. Analysis was based on the constant-comparison approach. RESULTS: Individuals described decision-making, illness actions and the medical encounter. The process was complicated by inadequate knowledge about symptoms, common-sense understandings about the GP appointment, difficulties concerning access to specialists, and patient-practitioner interactions. Autonomous decision-making and procedural justice were compromised. The accounts revealed contradictions between the policy ideals of active self-management, patient-centred care and shared decision-making, and the everyday experiences of individuals. CONCLUSIONS: For ethical healthcare there is a need for: public knowledge about early RA symptoms; more effective patient-practitioner communication; and increased support during the wait between primary and secondary care. Healthcare facilities and the government may consider different models to deliver services to people requiring rheumatology consults.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".