Exploring Factors Affecting the Rollout of a Policy on Registered Advanced Nurse Practitioners in Ireland
Bibliographic record
Abstract
Aim. To identify the barriers and enablers to the implementation of a national policy to increase and develop the advanced nurse practitioner (ANP) workforce in Ireland. Background. The Department of Health (Ireland) introduced a policy to increase the number of ANPs to 2% of the nursing workforce. Evaluation provides information to inform successful policy implementation and development of ANP roles in healthcare services. Methods. Qualitative descriptive design. Twenty candidate ANPs participated in four focus groups. Nine key stakeholders were also interviewed. Results. Analysis identified four barriers: lack of infrastructural resources; delay in releasing and arranging replacements for candidate ANPs; role resistance from administration, allied healthcare professionals and other nurses; and lack of organisational readiness. The five enablers were: supportive physicians; Nursing and Midwifery Practice Development Units; supportive directors of nursing; role awareness and clarity; and educational preparation. Conclusions. This evaluation identifies barriers and enablers to the implementation of a national policy to increase the critical mass of advanced practitioners within the healthcare services. Evaluation at the implementation phase informed the roll-out of future advanced practice initiatives. Implications for Nursing Management. To support advanced practice development, leadership, infrastructure, and resource planning are needed to harness known enablers and address identified barriers to the implementation and sustainability of these posts.
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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 | 0.093 | 0.207 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".