Models and Interventions to Promote and Support Engagement of First Nations Women with Maternal and Child Health Services: An Integrative Literature Review
Bibliographic record
Abstract
BACKGROUND: Studies show that participation in maternal and child health (MCH) services improves health outcomes for First Nations families. However, accessing MCH services can be associated with fear, anxiety, and low attendance at subsequent appointments. OBJECTIVE: To identify the existing knowledge of models/interventions that support engagement of First Nations women with MCH services in the child's first five years. METHODS: An integrative review was undertaken of full-text, peer-reviewed journal articles and grey literature, which were analysed to identify barriers and enabling factors that influenced the engagement of First Nations families with MCH services. RESULTS: Enabling factors that influenced the engagement with MCH services included service models/interventions that are timely and appropriate, and effective integrated community-based services that are flexible, holistic, culturally strong, and encourage earlier identification of risk and further assessment, intervention, referral, and support from the antenatal period to the child's fifth birthday. Barriers to engagement included inefficient communication, lack of understanding, cultural differences between the client and the provider, poor continuity of care, limited flexibility of service delivery to meet individual needs, and a health care model that does not recognise the importance of the social determinants of health and wellbeing. DISCUSSION: Timely, effective, holistic engagement with First Nations women during their child's first 2000 days, which respects their culture and facilitates genuine partnerships built on co-design and shared decision making with the indigenous community, needs to be an essential part of the MCH service model if health care providers seek to practice within First Nations communities. CONCLUSION: Improving engagement with MCH services is important for First Nations families, nursing practice, and public health.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".