How do health practitioners in a large Australian public hospital identify and respond to reproductive abuse? A qualitative study
Bibliographic record
Abstract
OBJECTIVE: Reproductive abuse is defined as a deliberate attempt to control or interfere with a woman's reproductive choices. It is associated with a range of negative health outcomes and presents a hidden challenge for health practitioners. There is a dearth of research on reproductive abuse, particularly qualitative research. This study aims to address this gap by exploring how health practitioners in a large Australian public hospital identify and respond to reproductive abuse. METHODS: We conducted semi-structured interviews with n=17 health practitioners working across multiple disciplines within a large metropolitan public hospital in Victoria. Data were analysed thematically. RESULTS: Three themes were developed: Figuring out that something is wrong; Creating a safe space to work out what she wants; and Everyone needs to do their part. CONCLUSIONS: Practitioners relied on intuition developed through experience to identify reproductive abuse. Once identified, most practitioners described a woman-led response promoting safety; however, there were inconsistencies in how this was enacted across different professions. Lack of clarity around the level of response required was also a barrier. Implications for public health: Our findings highlight the pressing need for evidence-based guidelines for health practitioners and a 'best practice' model specific to reproductive abuse.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".