Towards ‘Formalising’ WhatsApp Teledermatology Practice in KZ-N District Hospitals: Key Informant Interviews
Bibliographic record
Abstract
INTRODUCTION: District hospitals in KwaZulu-Natal Province, South Africa, do not have onsite specialist dermatology services. Doctors at these hospitals use WhatsApp instant messaging to informally seek advice from dermatologists and colleagues before possible referral. They have expressed the need to formalise WhatsApp teledermatology. AIM: To determine the views and perspectives of clinicians on the feasibility and practicality of formalising the current WhatsApp-based teledermatology activities within the KwaZulu-Natal Department of Health Dermatology Service. METHODS: Key informant interviews with 12 purposively selected doctors at district hospitals and all 14 dermatologists in the KwaZulu-Natal dermatology service. Their views and perspectives on formalising the current informal use of WhatsApp for teledermatology were recorded, transcribed, and thematically analysed. RESULTS: Five primary themes (communication, usability, utility, process, and poor understanding of legal, regulatory, and ethical issues) and 22 sub-themes were identified. Clinicians wanted WhatsApp teledermatology to continue, be formalised, and be incorporated within the KwaZulu-Natal Department of Health, facilitated by the provision of practical guidelines addressing legal, regulatory, and ethical issues. CONCLUSIONS: These findings will be used to develop a policy brief, providing recommendations and proposed guidelines for formalising the teledermatology service. The findings and methods will be relevant to similar circumstances in other countries.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".