WHAT IS THE ROLE OF COMMUNITY PREFERENCE INFORMATION IN HEALTH TECHNOLOGY ASSESSMENT DECISION MAKING? A CASE STUDY OF COLORECTAL CANCER SCREENING
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to determine the role of community preference information from discrete choice studies of colorectal cancer (CRC) screening in health technology assessment (HTA) reports and subsequent policy decisions. METHODS: We undertook a systematic review of discrete choice studies of CRC screening. Included studies were reviewed to assess the policy context of the research. For those studies that cited a recent or pending review of CRC screening, further searches were undertaken to determine the extent to which community preference information was incorporated into the HTA decision-making process. RESULTS: Eight discrete choice studies that evaluated preferences for CRC screening were identified. Four of these studies referred to a national or local review of CRC screening in three countries: Australia, Canada, and the Netherlands. Our review of subsequently released health policy documents showed that while consideration was given to community views on CRC, policy was not informed by discrete choice evidence. CONCLUSIONS: Preferences and values of patients are increasingly being considered "evidence" to be incorporated into HTA reports. Discrete choice methodology is a rigorous quantitative method for eliciting preferences and while as a methodology it is growing in profile, it would appear that the results of such research are not being systematically translated or integrated into HTA reports. A formalized approach is needed to incorporate preference literature into the HTA decision-making process.
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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 | 0.075 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".