[Perception of the gastroenterologist about the needs of continuing medical education in Peru].
Bibliographic record
Abstract
OBJECTIVE: To determine the perception of the gastroenterologist about the needs of continuing medical education (CME) in Peru. MATERIAL AND METHODS: Cross-sectional and descriptive study. The sample was not probabilistic. A survey was applied to the gastroenterologists members of the Society of Gastroenterology of Peru. The questionnaire was developed based on the "Canadian Association of Gastroenterology Educational Needs Assessment Report" with a Likert scale of 5 points (1 = not necessary and 5 = indispensable). The average of the scores obtained in each of the 33 items of the clinical, endoscopic and learning methods areas was determined. RESULTS: There were 75 participants and the average age was 43.40 years (SD ± 10.22 years). The place of work was mainly Lima (68%) and the majority (50.67%) had a service time of less than 5 years. The perception of educational needs in the clinical area was higher for gastric cancer (4.37 ± 0.87) and colon cancer (4.37 ± 0.83); in the endoscopic area were polypectomy (4.15 ± 0.95) and emergency techniques (4.13 ± 0.99). The main learning methods for gastroenterologists were attendance at congresses (4.29 ± 0.83) and endoscopic workshops (4.19 ± 1.06). CONCLUSIONS: The perception of the gastroenterologist surveyed on the needs of CME was mainly on gastric and colon cancer issues. Most of them considered congress attendance as the main learning method.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".