Outcomes of guidelines from health technology assessment organizations in community-based primary care: a systematic mixed studies review
Bibliographic record
Abstract
BACKGROUND: Health technology assessment (HTA) organizations generate guidelines to inform healthcare practices toward improved health outcomes. This review sought to identify and classify outcomes of guidelines from HTA organizations within published research. METHODOLOGY: We performed a systematic mixed studies review of empirical studies that (a) referred to a published guideline from an HTA organization and (b) reported an outcome resulting from a guideline. We searched the published literature in English or French within seven databases. Outcome types were classified within five dimensions of an existing framework for online health information (e.g., relevance, cognitive/affective impact, and use). Subdimensions were inductively developed. A two-phase sequential data synthesis was performed. Phase 1: a hybrid deductive-inductive thematic analysis identified the types of outcomes and displayed their relationships on a concept map. Phase 2: descriptive statistics were tabulated by the type of outcome. RESULTS: A total of 6,719 records were retrieved through searches on 6 February 2023. After screening, we included 120 observational studies (twenty-one qualitative, ninety-four quantitative, and five mixed methods). Phase 1 identified twenty-nine types of outcomes. The most frequently reported outcomes were within the organizational dimension (reported in ninety-four studies). The most common subdimensions were "Referrals" (thirty-eight occurrences), the "Quality of Prescriptions" (fifteen occurrences), and the "Quality of Diagnosis" (eight occurrences). For Phase 2, we could only generate descriptive statistics on seventeen outcomes. These were almost equally distributed among positive, neutral, and negative effects. Our results contribute to knowledge about the outcomes of HTA guidelines and options for documenting and measuring them in future evaluations.
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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 | 0.088 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.024 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".