© 2010 Canadian Medical Association or its licensors
Bibliographic record
Abstract
Gaps continue to exist between evidence generated byclinical research and practice.1 Efforts to improveaccess to health information in low- and middle-income countries2 and a greater knowledge of how to support the use of research evidence in clinical practice have made little difference. The health consequences of these gaps can be par-ticularly profound when highly effective interventions exist. For example, in the 42 countries in which 90 % of the deaths involving children worldwide occurred in 2000, nearly 2.2 mil-lion deaths among those under five years of age could have been prevented through the universal use of oral rehydration therapy in those with diarrhea and the use of insecticide-treated materials to prevent malaria.3 We conducted this study to examine the use of research-based evidence in defined clinical areas in a sample of health care providers in 10 low- and middle-income countries. We also examined factors that may facilitate or impede such use. Methods Study participants Our survey was part of a larger project that sought to explore factors that explain whether and how producers and users of research — health care providers and policy-makers — support the use of, or use, research-based evidence for decision-mak-ing. We surveyed health care providers in 10 low- and middle-
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.814 | 0.573 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".