Is Consent Not a Consideration for Instant Messaging?
Bibliographic record
Abstract
Background: Recently there has been a steady increase in the use of Instant Messaging (IM) as a means of providing health and healthcare services. This growth has been particularly rapid during the ongoing COVID-19 pandemic. Many reports indicate informal services using IM, in particular WhatsApp, have arisen spontaneously, in the absence of any formal guidelines and little consideration of consent. This study documents the consent practices of healthcare professionals using IM for clinical activities in District Hospitals in KwaZulu-Natal, South Africa and compares these practices with the literature. Methods: As part of a larger audit of telemedicine activity in KwaZulu-Natal a survey questioned clinicians’ use of IM, including consent practices and awareness of regulatory guidelines. Concomitantly multiple electronic databases were searched for papers on WhatsApp use in clinical service. Inclusion criteria were: papers written in English, reported on WhatsApp in clinical use or potential clinical use, and addressed consent. Results: The survey confirmed anecdotal reports of widespread informal use of WhatsApp in District Hospitals. Most clinicians were unaware of regulatory guidelines, and few obtained consent for taking photographs or sharing of images and information with colleagues for consultation. The literature review found that consent was mentioned in only 28 papers. Of these 11 reported that written consent was obtained, of which 5 were for taking photographs and 4 for sharing information with colleagues. Discussion: The survey showed that more than half of the respondents who used IM did not consider this to be telemedicine, with the corresponding ethical requirements governed by national guidelines, thereby risking legal exposure. However, South Africa’s regulatory guidelines do not align with common clinical practice. The literature shows that the majority of doctors shared patient information by IM without obtaining any form of consent. Conclusion: Practical guidelines are urgently required in South Africa and worldwide that balance practical conduct of medical care with sound contemporary ethical principles. Prudent guidance will ensure clinicians do not inadvertently breach patient privacy and confidentiality laws whilst permitting continued health-related use of instant messaging.
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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 | 0.244 | 0.626 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.016 | 0.020 |
| 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".