Changing behaviour in pregnant women: A scoping review
Bibliographic record
Abstract
OBJECTIVES: Behaviour change programmes (BCPs) for pregnant women are frequently implemented as part of health promotion initiatives. At present, little is known about the types of behaviour change programmes that are being implemented and whether these programmes are designed and delivered in accordance with the principles of high quality maternity care. In this scoping review, we provide an overview of existing interventions related to behaviour change in pregnancy with a particular emphasis on programmes that include empowerment components to promote autonomy and woman-led decision-making. METHODS: A systematic search strategy was applied to check for relevant papers in August 2017 and again in October 2018. RESULTS: Thirty studies met the criteria for inclusion. These studies addressed weight management, smoking cessation, general health education, nutrition, physical activity, alcohol consumption and dental health. The main approach was knowledge gain through education. More than half of the studies (n = 17) included three or more aspects of empowerment as part of the intervention. The main aspect used to foster women`s empowerment was skills and competencies. In nine studies midwives were involved, but not as programme leaders. CONCLUSIONS: Education for knowledge gain was found to be the prevailing approach in behaviour change programmes. Empowerment aspects were not a specific focus of the behaviour change programmes. This review draws attention to the need to design interventions that empower women, which may be beneficial through their live. As midwives provide maternal healthcare worldwide, they are well-suited to develop, manage, implement or assist in BCPs.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".