Assessment of a multimedia-based prospective method to support public deliberations on health technology design: participant survey findings and qualitative insights
Bibliographic record
Abstract
BACKGROUND: Using a combination of videos and online short stories, we conducted four face-to-face deliberative workshops in Montreal (Quebec, Canada) with members of the public who later joined additional participants in an online forum to discuss the social and ethical implications of prospective technologies. This paper presents the participants' appraisal of our intervention and provides novel qualitative insights into the use of videos and online tools in public deliberations. METHODS: We applied a mixed-method study design. A self-administered survey contained open- and close-ended items using a 5-level Likert-like scale. Absolute frequencies and proportions for the close-ended items were compiled. Qualitative data included field notes, the transcripts of the workshops and the participants' contributions to the online forum. The qualitative data were used to flesh out the survey data describing the participants' appraisal of: 1) the multimedia components of our intervention; 2) its deliberative face-to-face and online processes; and 3) its perceived effects. RESULTS: Thirty-eight participants contributed to the workshops and 57 to the online forum. A total of 46 participants filled-in the survey, for a response rate of 73 % (46/63). The videos helped 96 % of the participants to understand the fictional technologies and the online scenarios helped 98 % to reflect about the issues raised. Up to 81 % considered the arguments of the other participants to be well thought-out. Nearly all participants felt comfortable sharing their ideas in both the face-to-face (89 %) and online environments (93 %), but 88 % preferred the face-to-face workshop. As a result of the intervention, 85 % reflected more about the pros and cons of technology and 94 % learned more about the way technologies may transform society. CONCLUSIONS: This study confirms the methodological feasibility of a deliberative intervention whose originality lies in its use of videos and online scenarios. To increase deliberative depth and foster a strong engagement by all participants, face-to-face and online components need to be well integrated. Our findings suggest that online tools should be designed by considering, one the one hand, the participants' self-perceived ability to share written comments and, on the other hand, the ease with which other participants can respond to such contributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.091 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".