Preparación pedagógica y producción científico educacional en profesores de Salud Pública
Bibliographic record
Abstract
Introduction: to achieve a true scientific management of the educational process it is required\nthat teachers and tutors have a solid background in pedagogy and didactics. Objective: to identify the pedagogical qualifications and the educational scientific production of\nteachers of the subject Public Health at the Hermanos Cruz University Polyclinic in Pinar del Río.\nMethod: a descriptive cross-sectional study was conducted in the first quarter of 2013 at the\nHermanos Cruz University Polyclinic in Pinar del Río. The universe consisted of all teachers who\nwere part of the 5th year teacher staff in that polyclinic (U=28). The sample consisted of the\n15 teachers who teach the subject Public Health. For data collection, a questionnaire was\nadministered to teachers; and individual labor agreements, teacher staff records and the files\nof the teaching and research department of the institution were reviewed.\nResults: the staff consists of 15 teachers; most of them are instructors who have completed\nthe pedagogy basic course. Educational or professional research and publications are minimal.\nThere are no Masters in Medical Education. It is evident the need for teaching actions that\nbenefit the pedagogical qualifications of teachers, an aspect that is undisputed.\nConclusions: it was found that the majority of teachers of the subject Public Health have\npassed the Basic Course of Pedagogy. However, their educational research and publications are\nminimal compared to the rest of the scientific topics.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".