Foreword: Maternal and childbirth health systematic reviews
Bibliographic record
Abstract
From evidence to practiceSystematic reviews provide evidence about the effectiveness of many health interventions.However, translation of this evidence into routine health care is often not happening.1,2 The mere existence of evidence likely to improve patient care and health outcomes does not ensure that providers are informed and change their practices.3 Traditional strategies, such as passive dissemination of evidence via online bibliographical databases and peer-reviewed journals or distribution of educational materials, are not sufficient to change clinicians' practices.4 Implementation strategies of evidence-based guidelines show only a modest improvement of care.5,6 Studies assessing these dissemination and implementation strategies are extremely heterogeneous in their effects and methods used, 5 and their number is also limited.Thus, reviews of evidence performed from these studies are inconclusive.7,8 We know 'what works,' but major efforts need to be made to better understand 'how to disseminate and implement' this knowledge into practice.Adoption of new practices by health care providers is a complex event that depends not only on access to sound evidence but also on a variety of other factors, such as the nature and cost-benefit of the interventions for the patients, the clinicians and the health care system, the clinical setting, the individual characteristics, the social context, and other organisational and institutional factors.Identification of these multi-level factors is essential to develop effective dissemination and implementation strategies.In this endeavour, behavioural and social scientists play a key role in understanding how behavioural changes in clinicians can be successfully achieved.3 Further research, using rigorous methods and randomised designs, is needed to develop appropriate and effective interventions that integrate components of behavioural change theories.5 Moving towards more evidence-based dissemination and implementation will increase the impact of evidence and ultimately help to improve health care.The success of this research depends largely on a multi-disciplinary approach to developing strong theoretical models of behavioural changes adapted to health care settings.It is time for the dissemination and implementation of evidence-based medicine to become evidence based.
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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 | 0.024 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.100 | 0.035 |
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".