Exploring the healthcare journey of patients with rheumatoid arthritis: A mapping project – implications for practice
Bibliographic record
Abstract
OBJECTIVE: Consumers of healthcare can reveal important insights into the personal challenges they experience when negotiating their health needs. The National Rheumatoid Arthritis Society (NRAS) wanted to explore the experiences of those with rheumatoid arthritis (RA) in order to understand the impact on the individual and on healthcare resources and benchmark care against published standards and guidelines. METHODS: A project was designed to explore the experiences of individuals with sero-positive RA who had been diagnosed for three years or less. Qualitative semi-structured interviews were used and combined with process mapping to explore the experiences of a purposeful sample of individuals with RA. The information generated was mapped and variances explored. Ethical approval was not required as the data were collected outside the National Health Service. RESULTS: Twenty-two participants' stories were mapped. Fifty per cent of participants sought a medical opinion within three weeks of symptom onset and the majority received a disease-modifying anti-rheumatic drug within six months from first presenting symptoms. Work-related issues were highlighted by 13 participants, and seven of these experienced job losses directly attributed to their diagnosis. CONCLUSIONS: This unique mapping approach used qualitative research and process mapping to compare patient experiences against recognized standards and guidelines. These twenty-two stories reveal important insights into the challenges experienced in negotiating these healthcare journeys and the impact upon the individual as a result of variances in standards of care received. The participants in this study were chiefly self-motivated, informed and articulate, and did not reflect the broad ethnic, social or cultural diversity in the UK. Limitations must also be considered in relation to perceptions and recall of participants over a three-year period, as these may have altered over time and illness experience.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".