Patient Learning Pathway: Identifying Patient Competencies in Teledermatology for Effective Management of Dermatological Conditions
Bibliographic record
Abstract
BACKGROUND: Dermatology consultations in Québec, Canada, face accessibility challenges, with most dermatologists concentrated in urban areas. Teledermatology, offering remote diagnosis and treatment, holds promise in overcoming these limitations. However, concerns regarding patient-doctor relationships and logistical issues exist. OBJECTIVES: This article aims to introduce a dermatology patient learning pathway (PLP) developed by the Centre of Excellence on Partnership with Patients and the Public (CEPPP), focusing on knowledge, abilities, and skills mobilized by patients and their loved ones at key moments of the life course with an illness, as well as emerging educational needs. METHODS: The PLP development was co-developed with dermatology patient and caregiver partners, stakeholders, and the CEPPP team. The process encompassed stakeholder engagement, exploration, recruitment of patient and caregiver partners, co-development of the PLP draft, and validation through consensus building. RESULTS: The PLP methodology led to the creation of 44 learning objectives, comprising a total of 107 subobjectives. These objectives were organized into 8 phases of the patient life course with a dermatological condition: (1) prevention and predisposition; (2) discovery, self-examination, or observation of a change; (3) first consultation; (4) wandering; (5) consultation with a dermatologist; (6) diagnosis; (7) treatments; and (8) living with it. CONCLUSIONS: The dermatology PLP serves as a resource outlining patient competency across different stages of managing a dermatological condition throughout their life course. In the context of teledermatology, the PLP might facilitate patient and caregiver engagement by helping select appropriate information and tools to support active participation in care.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".