Health technology assessments conducted in health care facilities: A strategic practice? Findings from a content analysis of HTA reports
Bibliographic record
Abstract
In this paper, we test the hypothesis that health technology assessment units located in hospitals tend to be more optimistic toward technologies that are currently in use in their organization than technologies that are not. The data include 108 health technologies assessed in 87 full-scale health technology assessment reports produced by the four main local health technology assessment units in Quebec (Canada) on behalf of decision makers from the same facility. We found that 58 (53.7 percent) of the 108 technologies were currently in use within the hospital during their assessment. Based on the assessors' interpretation of the scientific evidence regarding the efficacy of the technologies, 67.3 percent of the technologies that were in use in the hospital during the evaluation were effective (56 percent for those that were not currently being used), but the difference is not statistically significant (chi-square 1.38; p = 0.24). Controlling for the efficacy judgment, the type of technologies (i.e. preventive, diagnostic, therapeutic or organizational), the number of technologies assessed in the report and the assessment unit, we found that the technologies that were currently in use in the facility during the evaluation were 62 percent more likely to be recommended favorably by the assessment unit than the technologies that were not currently being used (RR = 1.62; 95 percent CI = 1.06-1.88). This suggests that the local health technology units that were examined in the study tended to be more optimistic toward technologies that were currently in use in their hospital at the time of the evaluation.
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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 | 0.087 | 0.377 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".