Progress and challenges for advanced practice nursing in Mexico and the United Kingdom
Bibliographic record
Abstract
AIM: The aim of this study is to compare the advanced practice nursing development in Mexico with the United Kingdom. BACKGROUND: In spite of the involvement of global and local bodies to establish and develop advanced practice nursing worldwide, progress remains variable due to the lack of homogeneity in health care systems and policies. EVALUATION: Using thematic analysis from interviews of 29 health care professionals in Mexico, we identified four major issues that impact on the development of advanced practice nursing: (a) workforce, (b) organizational and institutional, (c) regulatory and legal and (d) academic and educational. KEY ISSUES: Learning from the UK experience in relation to overcoming some of these issues has been insightful in terms of how advanced practice nursing skills in Mexican nurses can be developed. CONCLUSIONS: Mexico is still in early stages of the development of APN. Based on the UK experience, the government may have to move forward to support higher level training, create labour market positions, establish new nursing functions, promote task-shifting and particularly implement solid regulation. IMPLICATIONS FOR NURSING MANAGEMENT: The development of advanced practice nursing represents important challenges for training and practice of nursing in Mexico and the United Kingdom; therefore, interested actors will have to reach key agreements that could work as the foundations of an assertive planning process.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".