Teledermatology scale-up frameworks: a structured review and critique
Bibliographic record
Abstract
BACKGROUND: The South African public health sector embarked on a National Telemedicine System implementation program in 1999 and although unsuccessful, the Province of KwaZulu-Natal subsequently implemented teledermatology in 2003, with two currently active services (synchronous and asynchronous). Although sustained these have not been scaled-up to meet the needs of all hospitals in the Province. A recent teledermatology scale-up design requirements elicitation process within KwaZulu-Natal confirmed the need for a framework, and identified requirements through key stakeholders, programme observations, the literature, and experts. This study aimed to identify and characterise existing teledermatology or related eHealth scale-up frameworks, determine whether any met the previously elicited scale-up framework requirements, and were suitable for use in the KwaZulu-Natal public health sector. METHODS: A structured literature search was performed of electronic databases (Scopus, Science Direct, IEEE, PubMed, and Google Scholar) seeking proposed or developed teledermatology or related scale-up frameworks. Global public health publications were also hand-searched. The teledermatology or telemedicine, telehealth or eHealth related scale-up frameworks identified were critiqued against the previously elicited teledermatology scale-up framework requirements to determine their suitability for use. RESULTS: No specific teledermatology scale-up framework was found. Seven related scale-up frameworks were identified, although none met all the previously identified teledermatology scale-up framework requirements. The identified frameworks were designed for specific scale-up phases and lacked a more holistic and comprehensive approach. CONCLUSIONS: There is an evidenced-based need for the development of a health sector aligned, holistic framework that meets the identified teledermatology scale-up framework requirements. The findings of this paper will inform development of such a framework.
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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 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.036 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".