“It’s not just hacking for the sake of it”: a qualitative study of health innovators’ views on patient-driven open innovations, quality and safety
Bibliographic record
Abstract
BACKGROUND: Open do-it-yourself (DIY) health innovations raise new dilemmas for patient-oriented and service-oriented scholars and healthcare providers. Our study aimed to generate practical insights into quality and safety issues to patient care raised by two volunteer-run, open DIY solutions: Nightscout Project (patient-driven, open-source software for type 1 diabetes management) and e-NABLE (volunteers who design and three-dimensionally print upper-limb assistive devices). To this end, we examined the views of health innovators who are knowledgeable about medical devices standards and regulations. METHODS: We applied a multimedia-based, data-elicitation technique to conduct indepth interviews with a diversified sample of 31 health innovators practising in two Canadian provinces (Quebec and Ontario). An exploratory thematic analysis approach was used to identify respondents' reasoning processes and compare their overall judgements of Nightscout and e-NABLE. RESULTS: Respondents pondered the following quality and safety issues: importance of the need addressed; accessibility; volunteers' ability to develop and maintain a safe solution of good quality; risks involved for users; consequences of not using the solution; and liability. Overall, innovators see Nightscout as a high-risk DIY solution that requires expert involvement and e-NABLE as a low-risk one that fills a hard-to-meet gap. CONCLUSION: Health innovators generally support patient-driven initiatives but also call for the involvement of professionals who possess complementary skills and knowledge. Our findings provide a list of issues healthcare providers may discuss with patients during clinical consultations to document potential risks and benefits of open DIY solutions. To inform new policy approaches, we propose the development of publicly funded umbrella organisations to act as intermediaries between open DIY solutions and regulatory bodies to help them meet quality and safety standards.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".