Transition to practice: Supporting first year nurses within a collaborative faith based graduate program
Bibliographic record
Abstract
Objective: Faith-based organisations play a major role in health care in Australia providing a unique service supported by compassionate and concerned staff. In response to the changing Australian health care landscape the increasing demands placed on first year registered nurses, a graduate program provided in partnership with a Catholic University, engages students in academic and clinical learning. The study aimed to determine if the provision of nursing care in the context of catholic faith and values provides first year nurses with a supportive learning environment.Methods: This study used a mixed method explanatory sequential design in two phases: (1) quantitative online surveys sent to graduate nurses (n = 60) to report on their perceptions of work integrated learning prior to and during their first year of nursing at the private catholic hospital; and (2) focus groups were conducted to explore key themes in further detail. The evaluation occurred at both the halfway and the end point of the 12-month Graduate Program. Data was analysed using descriptive statistics and theming of the text data to identify emergent ideas.Results: The findings suggest that the graduate nurses felt engaged with the programs academic and clinical learning outcomes. This was achieved in a supportive pastoral care environment underpinned by catholic faith and values.Conclusions: The Graduate Program in collaboration with a Catholic University School of Nursing and Midwifery has provided a positive learning experience and support structure for its first year registered nurses with the achievement of a formally recognised qualification.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".