Evolución histórica de la formación de los recursos humanos en Enfermería en Pinar del Río. 1961-2004
Bibliographic record
Abstract
Introduction: the program of training of human resources in Nursing undertaken in recent years has allowed the graduation of a significant number of nurses who have increased in quantity and quality for years as a necessary and possible way to raise the quality of the training of human resources in health. Objective: to describe the historical evolution of human resources training in Nursing in Pinar del Rio from 1961 to 2004. Method: probabilistic sampling of the target group was made up of 100 professors and 80 graduates, for a total of 180. Non-standardized interviews and questionnaires were applied having a historical cultural approach that allowed analyzing the origin and development of the evolution of the training of nurses. Descriptive statistics was the method used. Results: the period from 1965 to 1976 concluded with the graduation of 1 876 graduates in the first School of Nursing and at Marina Ascuy Labrador in the period of 1975-2004. After this period they are trained at Simon Bolivar Health Polytechnic Institute and conclude with the graduation of 5 757 nursing technicians, which made a great impact on the contribution of human resources. Conclusions: the training of human resources in nursing increases the quality of care to people, family and community; it is a priority in the country. In Pinar del Rio its beginning included the formation of Nursing Assistants and Technicians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".