Improving interagency service integration of the Australian Nurse Family Partnership Program for First Nations women and babies: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The Australian Nurse Family Partnership Program (ANFPP) is an evidence-based, home visiting program that offers health education, guidance, social and emotional support to first-time mothers having Aboriginal and/or Torres Strait Islander (First Nations) babies. The community-controlled sector identified the need for specialised support for first time mothers due to the inequalities in birthing and early childhood outcomes between First Nations' and other babies in Australia. The program is based on the United States' Nurse Family Partnership program which has improved long-term health outcomes and life trajectories for mothers and children. International implementation of the Nurse Family Partnership program has identified interagency service integration as key to program recruitment, retention, and efficacy. How the ANFPP integrates with other services in an Australian urban setting and how to improve this is not yet known. Our research explores the barriers and enablers to interagency service integration for the Australian Nurse Family Partnership Program ANFPP in an urban setting. METHODS: A qualitative study using individual and group interviews. Purposive and snowball sampling was used to recruit clients, staff (internal and external to the program), Elders and family members. Interviews were conducted using a culturally appropriate 'yarning' method with clients, families and Elders and semi-structured interview guide for staff. Interviews were audio-recorded and transcribed prior to reflexive thematic analysis. RESULTS: Seventy-six participants were interviewed: 26 clients, 47 staff and 3 Elders/family members. Three themes were identified as barriers and three as enablers. Barriers: 1) confusion around program scope, 2) duplication of care, and 3) tensions over 'ownership' of clients. Enablers (existing and potential): 1) knowledge and promotion of the program; 2) cultural safety; and 3) case coordination, co-location and partnership forums. CONCLUSION: Effective service integration is essential to maximise access and acceptability of the ANFPP; we provide practical recommendations to improve service integration in this context.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".