Disseminating health evidence summaries to increase evidence use in health care
Bibliographic record
Abstract
OBJECTIVE: To verify whether an intervention based on disseminating health evidence summaries by e-mail to health professionals increases access to health evidence databases, and whether health professionals intend to apply the evidence received by e-mail in their clinical practice. METHODS: This quantitative study started with a survey to collect demographic data and patterns of access to health evidence databases. It was followed by a longitudinal intervention, over 48 weeks, that disseminated 143 health evidence summaries to 339 health professionals with higher education degree who work in the Brazilian Unified Health System. In the longitudinal intervention phase, health professionals voluntarily assessed the received health evidence summaries using the information assessment method. Finally, the study concluded with a survey to identify changes in accessing health evidence databases. RESULTS: Of the 339 Brazilian health professionals participating in this research, 90 (26.5%) answered the initial and final surveys. After 48 weeks, there was an increase in the use of health evidence databases; 186 (54.9%) participants submitted 7,942 assessments of health evidence summaries, which were relevant for patient care in 5,409 (68%) assessments. CONCLUSIONS: The dissemination of health evidence summaries by e-mail to health professionals in Brazil increases the reported use of evidence in clinical practice.
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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 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".