Comparing the views of caseload midwives working with First Nations families in an all-risk, culturally responsive model with midwives working in standard caseload models, using a cross-sectional survey design
Bibliographic record
Abstract
PROBLEM: Little is known about midwives' views and wellbeing when working in an all-risk caseload model. BACKGROUND: Between March 2017 and December 2020 three maternity services in Victoria, Australia implemented culturally responsive caseload models for women having a First Nations baby. AIM: Explore the views, experiences and wellbeing of midwives working in an all-risk culturally responsive model for First Nations families compared to midwives in standard caseload models in the same services. METHODS: A survey was sent to all midwives in the culturally responsive (CR) model six-months and two years after commencement (or on exit), and to standard caseload (SC) midwives two years after the culturally responsive model commenced. Measures used included the Midwifery Process Questionnaire and Copenhagen Burnout Inventory (CBI). FINDINGS: 35 caseload midwives (19 CR, 16 SC) participated. Both groups reported positive attitudes towards their professional role, trending towards higher median levels of satisfaction for the culturally responsive midwives. Midwives valued building close relationships with women and providing continuity of care. Around half reported difficulty maintaining work-life balance, however almost all preferred the flexible hours to shift work. All agreed that a reduced caseload is needed for an all-risk model and that supports around the model (e.g. nominated social workers, obstetricians) are important. Mean CBI scores showed no burnout in either group, with small numbers of individuals having burnout in both groups. DISCUSSION AND CONCLUSION: Midwives were highly satisfied working in both caseload models, but decreased caseloads and more organisational supports are needed in all-risk models.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".