Design Requirements for a Teledermatology Scale-up Framework
Bibliographic record
Abstract
The value proposition of full-scale teledermatology is evidenced in the literature. The public health sector of South Africa’s KwaZulu-Natal province began synchronous teledermatology in 2003, followed by spontaneous asynchronous (mobile) teledermatology in 2013. No scale-up has been formally planned. This paper establishes design requirements that will inform the identification or de novo development of a teledermatology scale-up framework. Methods: A requirements definition process with inductive reasoning approaches was applied. Analysis of semi-structured interviews (19) with key teledermatology stakeholders (17) and observations from two teledermatology programmes, informed by lessons learned from prior teledermatology implementation attempts, eHealth scale-up literature and authors’ expert opinion, led to identification of Themes, and iterative reflection gave rise to Categories and Requirements. Results: Teledermatology scale-up framework design requirements emerged comprised of themes (4), categories (12), and specific design requirements (30). Discussion: This paper describes a process and resulting evidence-based (stakeholder interviews; programme observations; literature) and experience-based (expert opinion) design requirements to inform the identification and adoption / adaptation or de novo development of a teledermatology scale-up framework (TDSF) for KwaZulu-Natal’s public health sector. The proposed approach is recommended as a pre-requisite for scaling, including in other settings and for other telehealth applications.
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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 | 0.120 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".