Maternity connect: Evaluation of an education program for rural midwives and nurses
Bibliographic record
Abstract
BACKGROUND: Rural and regional health services often find it difficult to maintain their maternity service and skills of their maternity workforce and enable women to give birth close to home. The Maternity Connect Program is a professional development initiative aimed at supporting and upskilling rural and regional maternity workforces to meet their maternity population care needs. AIM: To evaluate the Maternity Connect Program from the perspectives and experiences of participating midwives/nurses and health services. METHODS: A retrospective audit of data routinely collected as part of the Maternity Connect Program: initial needs assessments (baseline survey), and one month and six months post-placement surveys completed by participants, placement health services and base health services. The main outcome measures were: participants' (midwives and health services) level of satisfaction with the Program; and changes in midwives'/nurses' perceived level of confidence in performing key midwifery skills after participating in the program. RESULTS: Respondents (n = 97 midwives/nurses; n = 23 base health services; n = 4 placement health services) were satisfied with the program and there was an increase in midwives/nurses' confidence when providing specific aspects of maternity care (birthing, neonatal and postnatal). Midwives/nurses report transferring skills learnt back to their base health service. CONCLUSION: The Maternity Connect Program appears to be a successful educational model for maintaining and increasing clinician confidence in rural and regional areas.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".