Changes in nurses' views and practices concerning nurse prescribing between 2006 and 2012: results from two national surveys
Bibliographic record
Abstract
AIMS: To assess changes in the prescribing practices and views about nurse prescribing of Registered Nurses in the Netherlands between 2006 and 2012. BACKGROUND: Considering the developments that took place in the Netherlands between 2006 and 2012, such as increased opportunities for nurse prescribing education and stricter control of nurses' prescribing practices, this study examines the extent to which nurses' prescribing practices and views have changed in the intervening years. In both years, nurses were not legally allowed to prescribe. DESIGN: Survey study. METHODS: Surveys were conducted in 2006 and 2012. Questionnaires were sent to a national sample of nurses. The questionnaires addressed nurses' views on nurse prescribing and the extent to which nurse prescribing took place in the respondents' work setting. RESULTS: There were 386 and 644 respondents to the 2006 and 2012 surveys respectively. The proportion of nurses who said that they felt adequately equipped to prescribe medicines remained constant around 12%. Insufficient knowledge to prescribe remained the most important reason for feelings of inadequacy. More than a quarter of the nurses in both surveys stated that nurses in their team sometimes write prescriptions. There were few changes in views on the consequences of nurse prescribing for nurses' practice. CONCLUSION: Overall, nurses' support for nurse prescribing remained stable at a fairly cautious level, while the number of nurses feeling inadequately equipped to prescribe remained high. As nurse prescribing is expected to improve the quality and continuity of care, this should be taken into account in policy expectations.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".