Symptom Appraisal and Help-Seeking Among Patients With Autoimmune Rheumatic Diseases: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: Long diagnostic delay remains an unsolved problem in many autoimmune rheumatic diseases (ARDs). One of the major contributing factors is poor symptom appraisal and the resulting delays in help-seeking by patients themselves. We therefore aimed to understand the symptom appraisal and help-seeking experience among patients with ARDs in a multiethnic urban Asian population and to explore its influencing factors. METHODS: Semistructured interviews with 33 patients with ARDs were audio recorded and transcribed verbatim. We coded the transcripts deductively using the reported 3 stages of symptom appraisal (detection, interpretation, and response) as the framework, and inductively for newly emerging themes and subthemes. RESULTS: All 3 stages of the symptom appraisal and help-seeking journey (ie, symptom detection [by self and by others], symptom interpretation [causes, consequences, and required actions] and symptom response [no action, self-management, seeking help from nonhealthcare professionals, and seeking help from healthcare professionals]) were observed among patients. Interactions among these 3 stages were also observed: symptom interpretation was found to influence subsequent symptom detection, and the outcome of symptom response was found to influence both subsequent symptom detection and symptom interpretation. Various personal and socioenvironmental factors (eg, knowledge and cultural beliefs about the symptom) that influenced symptom appraisal and help-seeking were identified from the interviews. CONCLUSION: The symptom appraisal and help-seeking journey of patients with ARDs is an iterative process of detection, interpretation, and response, and is influenced by various personal and socioenvironmental factors. Addressing modifiable factors could shorten the symptom appraisal and help-seeking interval and improve patient outcomes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".