Impact of tutorial activity on the desertion of dentistry students’ generations 2016-2020 at the Universidad Autónoma de Campeche
Bibliographic record
Abstract
Tutorial attention is one of the most valuable tools to reduce school dropout, through tutoring, it is possible to investigate the causes that lead the student to make the decision to drop out and propose solutions to various problems to support the student. Since 2015, the UAC School of Dentistry implemented changes in the tutoring program, and in order to know its effect on dropout, a retrospective cohort study was carried out, in which generations of the PE of Dental Surgeon of the Autonomous University of Campeche (UACAM) during the period 2016-2020, the dropout rates per semester were determined, and it was related to the tutorial activity during the study period, risk measures were also calculated related to sex, age and place of origin. It was found that tutorial attention is a protection factor against dropout, students who are attended have a 72% lower risk of dropping out than those who are not. Factors such as being male or female, age, and origin was not a risk factor for desertion in this population. We conclude that the strategies of the tutorial action plan have positive effects to prevent the dropout of students from the UACAM Dental Surgeon program
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".