The need for and value of nurse and midwife prescribing: Findings from an Irish research investigation
Bibliographic record
Abstract
AIM: The aim of this study is to determine the need for and value of nurse and midwife prescribing in Ireland as identified by these prescribers-the people most able to provide relevant insights and information. BACKGROUND: Since 2007, nurses and midwives in Ireland who have passed an additional educational program can prescribe medicinal products relative to their clinical practice areas. Research evidence of efficacy is needed now for prescribing sustainability in Ireland and to encourage, if successful, the adoption or expansion of frontline nurse/midwife prescribing rights in other countries. DESIGN: A qualitative study was undertaken. METHODS: Interviews with registered nurse and midwife prescribers were conducted in 2017 until data saturation. Constant-comparative coding and categorization of data revealed themes and categories, with explanatory quotes for research trustworthiness and credibility purposes. RESULTS: Six data themes emerged: (a) more than just writing prescriptions; (b) highly individualized evidence-based specialist care; (c) assured, timely and rapid accessibility to needed care; (d) health system and healthcare efficiency gains; (e) satisfaction with nurse/midwife prescriber services and (f) quality care improvements. CONCLUSION: Nurse/midwife prescribing in Ireland was identified as needed, safe, effective and cost-effective. Prescribing permitted accessible, thorough and proactive holistic health promotive care to be provided in nurse- or midwife-led outpatient clinics.
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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 | 0.028 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".