Factors That Influence Maternal Child Health Nurses’ Identification of Risk of Family Violence to First Nations Women in Australia
Bibliographic record
Abstract
Aim: To understand the factors that influence family violence towards First Nations women, to inform practises and policies to support these women and improve their engagement in maternal child health services. Design: A qualitative study, using narrative inquiry integrated with the Indigenous philosophy ‘Dadirri’, and thematic analysis of the data. Materials and Methods: Survey of 10 Maternal Child Health nurses in 2019, and interviews of 35 Aboriginal mothers in 2021. Results: The nurses identified drugs, alcohol, socio-economic issues, the history of effects of colonisation on First Nations peoples, and stress as perceived factors influencing family violence, and acceptance, fear, cultural beliefs, and mistrust, for women’s low reporting of violence. Factors that influenced nurses’ ability to identify family violence were mistrust and understanding of Aboriginal culture. Low self-esteem, lack of belonging, and not being heard were identified by the mothers as factors that influence family violence. Fear of child protective services, shame, mistrust, and poor rapport with the nurses contributed to their low reporting of violence. The most significant factor for the mothers to disclose violence is fear of losing her child, mistrust, and the questioning process. Conclusions: Nurses’ understanding of Indigenous culture is critical to develop trust and improve the engagement of First Nations women. A significant difference in the synthesis of data between the nurses and their First Nations consumers was conspicuous. Research regarding the benefit of models and interventions that recognise the social determinants of health and well-being on health outcomes as well as the value of culturally strong health services aimed to encourage an earlier identification of risk, ideally from the antenatal period to the child’s fifth birthday, is imperative. The implications of this research are of international importance for First Nations families and challenge current nursing practises to address the human rights challenge of the inequity in health outcomes between First Nation and non-First Nation children, their exposure to family violence, and their over-representation in child protection services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".