Development of a Conceptual Framework for a Patient-Reported Impact of Dermatological Diseases (PRIDD) Measure: A Qualitative Concept Elicitation Study
Bibliographic record
Abstract
Existing patient-reported outcome measures cannot comprehensively capture the full impact of living with a dermatological condition. The aim of this study was to develop a conceptual framework on which to build a new Patient-Reported Impact of Dermatological Diseases (PRIDD) measure. Adults (≥ 18 years of age) living with a dermatological condition, worldwide and/or representatives from a patient organization recruited via a global patient organization network, were invited to an individual or group interview. Data were analyzed thematically. Sixty-five people from 29 countries, representing 29 dermatological conditions, participated. Key themes were: (i) impacts at the individual, organizational and societal levels; (ii) impacts were point-in-time and cumulative; and (iii) impact is a multifaceted construct, with two subthemes (iiia) common impacts and (iiib) psychological and social impacts are most significant. The conceptual framework shows that impact is a multifaceted concept presenting across physical, psychological, social, financial, daily functioning and healthcare, and provides the basis for co-constructing the PRIDD with patients.
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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 | 0.082 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".