Targeting ethical considerations tied to image-based mobile health diagnostic support specific to clinicians in low-resource settings: the Brocher proposition
Bibliographic record
Abstract
Background: mHealth applications assist workflow, help move towards equitable access to care, and facilitate care delivery. They have great potential to impact care in low-resource countries, but have significant ethical concerns pertaining to patient autonomy, safety, and justice.Objective: To achieve consensus among stakeholders on how to address concerns pertaining to autonomy, safety, and justice among mHealth developers and users in low-resource settings, in particular for the application of image-based consultation for diagnostic support.Methods: A consensus approach was taken during a three-day workshop using a purposive sample of global mHealth stakeholders (n = 27) professionally and geographically spread. Throughout a series of introductory talks, group brainstorming, plenary reviews, and synthesis by the moderators, lists of actions were generated that address the concerns engendered by mHealth applications on autonomy, justice and safety, taking into account the development, implementation, and scale-up phases of an mHealth application lifecycle.Results: Several types of actions were recommended; key ones among them included building in risk mitigation measures from the development stage, establishing inclusive consultation processes, using open sources platform whenever possible, training all clinical users, and bearing in mind that the gold standard of care is face-to-face consultation with the patient. Recommendations of patient, community and health system participation and of governance were identified as cutting across the mHealth lifecycle.Conclusion: Priorities agreed-upon at the meeting echo those put forward concerning other domains and locations of application of mHealth. Those more forcefully articulated are the need to adopt and maintain participatory processes as well as promoting self-governance. They are expected to cut across the mHealth lifecycle and are prerequisites to the safeguard of autonomy, safety and justice.
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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 | 0.272 | 0.363 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.092 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.026 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 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".