Victorian maternal child health nurses’ knowledge, attitudes and beliefs towards national registration changes
Bibliographic record
Abstract
In 2010 National Registration for nurses was established which was likely to impact the role of the maternal and child health nurses (MCH) in Victoria. This study explored the perceived impact of the national changes to the MCH nurse workforce in Victoria following the implementation of national registration and a proposed national service framework. A qualitative exploratory descriptive design was employed with the purpose of exploring the knowledge, attitudes and beliefs of Key Stakeholders (KSH) to the recent changes and perceived impact to Victorian MCH nurses. The significance of this study lies with understanding the gaps in current knowledge of KSH to the national changes. Outlined briefly in this paper will be main findings from the KSH. This involved interviewing 12 KSH from management positions, including Local Government Coordinators, Policy Advisors to the Department of Education and Early Childhood Development, the Municipal Association of Victoria, along with academics from Universities that provide postgraduate Child and Family Health education programs for the MCH nurse qualification. Date was transcribed verbatim and content analysis used. Categories were developed by identifying recurrent patterns from the data, labels were then chosen which reflected the participant’s words: “common standard”; “losing our identity”; “universal service”; “we do it well” and “imposed from above”. Overall the KSH were concerned how the disparity in education and qualifications would be resolved and the effect this would have on the service. Findings from this study highlight the importance of comprehensively investigating services offered by all jurisdictions and using collaboration, communication and leadership to effectively introduce change.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".