Apreciaciones de los egresados de enfermería de la Universidad Libre de Pereira sobre aspectos académicos del programa
Bibliographic record
Abstract
Objective: Some characteristics of the graduates of the Libre University of Pereira were determined and their assessments about\nsome academic aspects of the program. Methods: A descriptive study of 64 nurses from a total of 235 graduates from the period\nbetween 2002 and the second quarter of 2008, to whom the survey was applied. Likert scales were used and percentages and\nmeans for qualifying studied academic aspects were calculated. Results: 91.0% of respondents had not engaged in postgraduate\nstudies. 84.1% said they were unaware of the mechanisms of participation and the involvement of program discussion bodies.\nThe academic program feature that received a higher score was integral training (mean 4.4), the academic areas that were\nconsidered to be stronger were clinical and administrative, and the less favored, communal (mean 2.9). The best valued aspects\nof job performance were: the ability to apply knowledge in holistic care and ethical practice of the profession. Skills in research\nand application of the methodology of the nursing process and theories of discipline, components of broad emphasis of the\nprogram, were rated low, explained by the reduced ability to exercise in these fields of knowledge. Recommendation: The\ndiffusion mechanisms of spaces for graduate participation should be improved, to strengthen program decisions, especially\nrelated to curriculum reform.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".