IMPACT OF HEALTH TECHNOLOGY ASSESSMENT REPORTS ON HOSPITAL DECISION MAKERS – 10-YEAR INSIGHT FROM A HOSPITAL UNIT IN SHERBROOKE, CANADA: IMPACT OF HEALTH TECHNOLOGY ASSESSMENT ON HOSPITAL DECISIONS
Bibliographic record
Abstract
OBJECTIVES: The overarching goal of this research was to (i) evaluate the impact of reports with recommendations provided by a hospital-based health technology assessment (HB-HTA) unit on the local hospital decision-making processes and implementation activities and (ii) identify the underlying factors of the nonimplementation of recommendations. METHODS: All reports produced by the HB-HTA unit between December 2003 and March 2013 were retrieved, and hospital decision makers who requested these reports were solicited for enrolment. Participants were interviewed using a mixed design survey. RESULTS: Twenty reports, associated with fifteen decision makers, fulfilled the study criteria. Nine decision makers accepted to participate, corresponding to thirteen reports and twenty-three recommendations. Of the twenty-three recommendations issued, 65 percent were implemented, 9 percent were accepted for implementation but not implemented, and 26 percent were declined. In terms of the utility of each report to guide decision makers, 92 percent of the reports were considered in the decision-making process; 85 percent had one or more recommendations adopted; and 77 percent had recommendations implemented. The most frequently mentioned reasons for nonimplementation were related to contextual factors (64 percent), production/diffusion process factors (14 percent), content/format factors (14 percent), or other factors (9 percent). Among the contextual factors, the complexity of the changes (i.e., administrative reasons), budget and resources constraints, failure to identify administrative responsibility to carry out the recommendation, and nonpriority status of the HTA recommendation, were provided. CONCLUSIONS: This study highlights that although HB-HTA reports are useful to hospital managers in their decision-making processes, certain barriers such as contextual factors need to be better addressed to improve HB-HTA efficiency and usefulness.
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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 | 0.042 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".