Examining the ethical and social issues of health technology design through the public appraisal of prospective scenarios: a study protocol describing a multimedia-based deliberative method
Bibliographic record
Abstract
BACKGROUND: The design of health technologies relies on assumptions that affect how they will be implemented, such as intended use, complexity, impact on user autonomy, and appropriateness. Those who design and implement technologies make several ethical and social assumptions on behalf of users and society more broadly, but there are very few tools to examine prospectively whether such assumptions are warranted and how the public define and appraise the desirability of health innovations. This study protocol describes a three-year study that relies on a multimedia-based prospective method to support public deliberations that will enable a critical examination of the social and ethical issues of health technology design. METHODS: The first two steps of our mixed-method study were completed: relying on a literature review and the support of our multidisciplinary expert committee, we developed scenarios depicting social and technical changes that could unfold in three thematic areas within a 25-year timeframe; and for each thematic area, we created video clips to illustrate prospective technologies and short stories to describe their associated dilemmas. Using this multimedia material, we will: conduct four face-to-face deliberative workshops with members of the public (n=40) who will later join additional participants (n=25) through an asynchronous online forum; and analyze and integrate three data sources: observation, group deliberations, and a self-administered participant survey. DISCUSSION: This study protocol will be of interest to those who design and assess public involvement initiatives and to those who examine the implementation of health innovations. Our premise is that using user-friendly tools in a deliberative context that foster participants' creativity and reflexivity in pondering potential technoscientific futures will enable our team to analyze a range of normative claims, including some that may prove problematic and others that may shed light over potentially more valuable design options. This research will help fill an important knowledge gap; intervening earlier in technological development could help reduce undesirable effects and inform the design and implementation of more appropriate innovations.
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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 | 0.231 | 0.174 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".