Identifying the need for care in rheumatoid arthritis: A Candidacy 2.0 analysis of lived experiences
Bibliographic record
Abstract
This study explores how individuals with rheumatoid arthritis (RA) come to identify themselves as candidates for medical care, using the newly developed Candidacy 2.0 model. Candidacy 2.0 extends the original Candidacy Framework by emphasizing the role of the embodied intersectional relational self in healthcare access, providing a theoretical framework that transcends specific healthcare systems. Through semi-structured interviews with 33 individuals living with RA across six Canadian provinces, we examined how embodied experiences, social identities, and relational contexts shape initial recognition of care needs. Analysis revealed distinct 'tipping points' where symptom progression from single to multiple joints and unmanageable pain forced recognition of care needs. Age and gender identities created distinct barriers to care-seeking: younger individuals dismissed symptoms as affecting only older adults, while gendered expectations about caregiving delayed help-seeking among women. Professional identity emerged as particularly significant, offering knowledge advantages but sometimes hindering patient-centered care. Support networks proved crucial in symptom interpretation and help-seeking, with their importance highlighted by COVID-19-related disruptions. The study demonstrates how Candidacy 2.0's emphasis on embodied, intersectional, and relational aspects of healthcare access enhances understanding of help-seeking behaviors in chronic conditions. Findings suggest the need for targeted public health campaigns addressing age-related misconceptions, gender-sensitive clinical approaches, flexible care delivery models that accommodate support networks, and educational resources helping patients identify and act upon significant symptom changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".