Success Indicators and Barriers to Acute Nurse Practitioner Role Implementation in Four Ontario Hospitals
Bibliographic record
Abstract
Changes in healthcare environmental factors resulted in the introduction of the acute care nurse practitioner (ACNP) role in Ontario. The purpose of the study was to identify success indicators, barriers, and recommendations for role implementation to assist healthcare providers to develop strategies for integrating ACNPs into teams. Acute care nurse practitioners (n = 14), physicians (n = 14), administrators (n = 12), and staff nurses (n = 48) from four tertiary care hospitals completed a researcher-developed, self-administered questionnaire with fixed and open-ended questions. Specialty practice areas (cardiac/critical care, geriatrics, and nephrology) were matched within the four sites. Acute care nurse practitioners (n = 14), physicians (n = 12), administrators (n = 8), and staff nurses (n = 34) responded. The major indicator by all groups for successful role implementation was level of preparation. Barriers included lack of mentorship and knowledge of the role, and perceived lack of support from administration and physicians. Themes reflecting impact on patient care were improved communication and attention to patient care issues. Respondents accepted the role, concluding that enhanced continuity of care was a result. Role clarity before and during implementation would assist team members in understanding the purpose and value of the role, thus easing the integration of the ACNP into the healthcare team.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".