What medical specialists like and dislike about health technology assessment reports
Bibliographic record
Abstract
OBJECTIVE: To examine how medical specialists view health technology assessment (HTA) and its role in policy-making. METHODS: Semi-structured interviews with 28 medical specialists practising in Quebec and Ontario (Canada) to examine their views on an HTA report relevant to their specialty (prostate-specific antigen screening, electroconvulsive therapy and prenatal screening for Down's syndrome). RESULTS: Medical specialists represent a particularly demanding audience for HTA producers because they are knowledgeable about current studies in their field and often contribute to the evidence base that HTA seeks to synthesize. In all three cases, specialists not only challenged specific points in the content of the HTA reports but also offered different and sometimes conflicting appraisals of the clinical relevance and policy implications. More than just the timeliness and usefulness of HTA findings are at issue. The views of specialists are grounded in a clinical understanding of what counts as evidence and how decisions should be made, a view that contrasts with the societal perspective of HTA. CONCLUSIONS: HTA producers cannot afford to overlook medical specialists who play a key role in the adoption of health technologies. Establishing a transparent dialogue between producers and users of HTA reports could enrich policy recommendations.
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 | 0.055 | 0.265 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".