Implementation strategy for advanced practice nursing in primary health care in Latin America and the Caribbean
Bibliographic record
Abstract
SYNOPSIS Advanced practice nursing (APN) is a term used to describe a variety of possible nursing roles operating at an advanced level of practice. Historically, APN roles haves evolved informally, out of the need to improve access to health care services for at-risk and disadvantaged populations and for those living in underserved rural and remote communities. To address health needs, especially ones related to primary health care, nurses acquired additional skills through practice experience, and over time they developed an expanded scope of practice. More recently, APN roles have been developed more formally through the establishment of graduate education programs to meet agreed-upon competencies and standards for practice. The introduction of APN roles is expected to advance primary health care throughout Latin America and the Caribbean, where few such roles exist. The purpose of the paper is to outline an implementation strategy to guide and support the introduction of primary health care APN roles in Latin America and the Caribbean. The strategy includes the adaptation of an existing framework, utilization of recent research evidence, and application of knowledge from experts on APN and primary health care. The strategy consists of nine steps. Each step includes a national perspective that focuses on direct country involvement in health workforce planning and development and on implementation. In addition, each step incorporates an international perspective on encouraging countries that have established APN programs and positions to collaborate in health workforce development with nations without advanced practice nursing.
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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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".