Understanding factors affecting the integration of geriatric nurse practitioners into health systems
Bibliographic record
Abstract
BACKGROUND: Geriatric nurse practitioners (NPs) are introduced into health systems to alleviate provider shortages and improve care for older adults. To achieve these goals, geriatric NPs must be integrated into the health system such that they can efficaciously practice. Internationally, little is known about factors affecting the integration of NPs. Such evidence would improve policymaking and the impact of geriatric NPs on care. In Israel, geriatric NPs were recently introduced. Their ongoing integration is an exemplar for other countries. PURPOSE: To identify factors affecting the integration of geriatric NPs in Israel and discuss application of these factors in international policy and research. METHODOLOGY: The Consolidated Framework for Implementation Research guided this qualitative descriptive study. A semistructured interview guide was used to collect data from four professional groups (geriatric NPs, physicians, administrators, and policymakers), which, together, provide a system-level perspective. Factors were identified using deductive content analysis and designated as facilitators, barriers, neutral, or mixed effects. RESULTS: There were 58 participants across the four professional groups. Twenty-eight factors were identified, including patient needs and leadership engagement (facilitators), available information (barrier), culture (mixed), and evidence strength (neutral). Perspectives on several factors differed by the professional group's role in integrating NPs (e.g., costs ). CONCLUSIONS: The barriers highlight lacking interprofessional support from a priori policymaking and communication breakdowns. Policies should reflect priorities of administrators, clinicians, and policymakers. IMPLICATIONS: These factors may inform policymaking in other countries but would be most effective if based on country-specific research. This implementation science approach may inform future studies.
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| 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".