How medical specialists appraise three controversial health innovations: scientific, clinical and social arguments
Bibliographic record
Abstract
Medical specialists play a pivotal role in health innovation evaluation and policy making. Their influence derives not only from their expertise, but also from their social status and the power of their professional organisations. Little is known, however, about how medical specialists determine what makes a health innovation desirable and why. Our qualitative study investigated the views of 28 medical specialists and experts from Quebec and Ontario (Canada) on three controversial innovations: electroconvulsive therapy, prostate-specific antigen screening and prenatal screening for Down's syndrome. Our findings indicate that the scientific, clinical and social arguments of medical specialists combine to create a relatively consistent narrative for each innovation. Our comparative analysis suggests that these narratives bring about a 'soft' resolution to controversies, which relies on a more or less tacit understanding of the social desirability of innovations and which sets the stage for their routinisation. Such an unpacking of medical specialists' arguments both for and against new technologies is needed because such arguments may easily be considered authoritative and because there are few forums for debating the social desirability of innovations not generally deemed to be highly controversial.
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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 | 0.047 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.053 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".