Habilidades investigativas en residentes de Anestesiología y Reanimación: una necesidad en su formación profesional
Bibliographic record
Abstract
Background: research skills are fundamental tasks in the teaching-learning process as they allow for the solution of health problems through the scientific method. The deficiencies in the development of research skills among Anesthesiology and Resuscitation residents led the authors to carry out this study.Objective: to characterize the research skills of Anesthesiology and Resuscitation residents in the province of Villa Clara, Cuba.Methods: a cross-sectional descriptive study was carried out at the Arnaldo Milián Castro University Clinical and Surgical Hospital in Santa Clara, from September 2023 to December 2024. Theoretical methods were used: historical-logical, analytical-synthetic, and inductive-deductive; empirical methods: document analysis and the technique for analyzing the results of the activity; mathematical-statistical methods: percentage calculations; and descriptive statistics, tables, and figures expressing the values of the variable attributes.Results: difficulties in the research skills of Anesthesiology and Resuscitation residents were identified. The most affected were: adequate literature review, use of bibliographic records, and operationalization of variables, written report preparation, and use of the Vancouver Standards.Conclusions: the research skills of Anesthesiology and Resuscitation residents in Villa Clara Province were characterized, allowing their main strengths and weaknesses to be identified.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".