Exploring registered nurses’ attitudes towards postgraduate education in Australia: An overview of the literature
Bibliographic record
Abstract
Normal 0 false false false EN-AU X-NONE X-NONE Background: Nursing education is designed to prepare competent nurses to meet the current and future health care needs of society. The nursing profession responds to changes in health care needs by exploring new methods for providing care, by changing educational emphases, and by establishing practice standards in new areas. Aim: This literature overview examines issues relating to postgraduate education for specialty nursing practice. Methods : For this literature review, the following computerised databases: CINAHL, PubMed, Medline, EMBASE, Scopus, ERIC, BERA, Cochrane and PsycINFO were used to identify journal articles, books and book chapters using key search terms in various combinations. Whilst there was no time limit imposed upon this search, a guiding evaluation framework and specific criteria did necessarily and purposefully de-limit the review. Results: As this review sought to examine registered nurses’ attitudes towards postgraduate education for specialty practice, the literature search was informed by studies that assessed only participants’ acquisition of knowledge and skills as well as changes in attitudes and behaviours. The articles and reports extracted through the initial literature search, and subjected to the exclusion criteria, were then reviewed and categorised according to the three themes developed from the modification to Barr et al . ’s Evaluative Framework. Conclusions: There are a number of issues associated with registered nurses’ attitudes to postgraduate education for specialty practice. The literature provides some insight into the benefits they perceive as accruing from such study.
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 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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".