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Record W4393347793 · doi:10.35429/h.2023.10.90.100

Impact of tutorial activity on the desertion of dentistry students’ generations 2016-2020 at the Universidad Autónoma de Campeche

2023· book-chapter· en· W4393347793 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECORFAN eBooks · 2023
Typebook-chapter
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsImpact
Fundersnot available
KeywordsNomaDesertionDentistryMedicineEngineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.344
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it