National nursing strategies in seven countries of the Region of the Americas: issues and impact.
Bibliographic record
Abstract
OBJECTIVE: To identify and examine the current national nursing strategies and policy impact of workforce development regarding human resources for health in seven selected countries in the Region of the Americas: Argentina, Canada, Costa Rica, Jamaica, Mexico, Peru, and the United States. METHODS: A review of available literature was conducted to identify publicly-available documents that describe the general backdrop of nursing human resources in these seven countries. A keyword search of PubMed was supplemented by searches of websites maintained by Ministries of Health and nursing organizations. Inclusion criteria limited documents to those published in 2008-2013 that discussed or assessed situational issues and/or progress surrounding the nursing workforce. RESULTS: Nursing human resources for health is progressing. Canada, Mexico, and the United States have stronger nursing leadership in place and multisectoral policies in workforce development. Jamaica shows efforts among the Caribbean countries to promote collaborative practices in research. The three selected countries in Central and South America championed networks to revive nursing education. Yet, overall challenges limit the opportunities to impact public health. CONCLUSIONS: The national nursing strategies prioritized multisectoral collaboration, professional competencies, and standardized educational systems, with some countries underscoring the need to align policies with efforts to promote nursing leadership, and others, focusing on expanding the scope of practice to improve health care delivery. While each country wrestles with its specific context, all require proper leadership, multisectoral collaboration, and appropriate resources to educate, train, and empower nurses to be at the forefront.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".