Public health interventions in midwifery: a systematic review of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Maternity care providers, particularly midwives, have a window of opportunity to influence pregnant women about positive health choices. This aim of this paper is to identify evidence of effective public health interventions from good quality systematic reviews that could be conducted by midwives. METHODS: Relevant databases including MEDLINE, Pubmed, EBSCO, CRD, MIDIRS, Web of Science, The Cochrane Library and Econlit were searched to identify systematic reviews in October 2010. Quality assessment of all reviews was conducted. RESULTS: Thirty-six good quality systematic reviews were identified which reported on effective interventions. The reviews were conducted on a diverse range of interventions across the reproductive continuum and were categorised under: screening; supplementation; support; education; mental health; birthing environment; clinical care in labour and breast feeding. The scope and strength of the review findings are discussed in relation to current practice. A logic model was developed to provide an overarching framework of midwifery public health roles to inform research policy and practice. CONCLUSIONS: This review provides a broad scope of high quality systematic review evidence and definitively highlights the challenge of knowledge transfer from research into practice. The review also identified gaps in knowledge around the impact of core midwifery practice on public health outcomes and the value of this contribution. This review provides evidence for researchers and funders as to the gaps in current knowledge and should be used to inform the strategic direction of the role of midwifery in public health in policy and 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 | 0.042 | 0.150 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".