A Survey of Telemedicine Use by Doctors in District Hospitals in KwaZulu-Natal, South Africa
Bibliographic record
Abstract
There is anecdotal evidence of informal telemedicine activity in KwaZulu-Natal (KZ-N), South Africa. AIM: To determine the current extent of telemedicine in district hospitals in KZ-N; the range of clinical activities and technologies used; additional services needed; current knowledge and practice regarding legal, ethical, and regulatory issues; and the need to formalise telemedicine activities. METHOD: A cross-sectional survey of telemedicine use by 143 doctors working at 22 District hospitals in KZ-N. RESULTS: Most doctors (96%) participated in some form of telemedicine across a spectrum of disciplines, but more than half did not consider their activities to constitute telemedicine. To meet their needs, doctors have started their own informal services with colleagues, using mostly instant messaging and chat groups (WhatsApp). Some doctors indicated the need to formalise these services and establish additional services. Few doctors were aware of the national telemedicine guidelines and the required written informed consent for telemedicine was seldom obtained. This could have serious legal, regulatory, and ethical implications. CONCLUSIONS: Practical clinical and technical guidelines and standard operating procedures need to be developed with the active participation of the clinical workforce. These should encourage innovation and greater use of telemedicine, including the use of instant messaging apps.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".