Student perception of gender biases and stereotypes present in their training as radiologists at a Chilean university: initial approach
Bibliographic record
Abstract
Introducción: La igualdad de género es fundamental en el ámbito educativo, y por ello la Universidad de Chile ha desarrollado acciones, políticas y reglamentos para avanzar hacia una docencia con enfoque de género, libre de estereotipos y de conductas sexistas. Objetivo: Conocer la percepción del estudiantado del programa de radiología sobre los sesgos y los estereotipos de género presentes en su formación, para implementar acciones de mejora. Material y métodos: Se efectuó un cuestionario online anónimo sobre el tema señalado. Para el análisis de los resultados se dicotomizaron las categorías de respuesta y se calcularon las frecuencias absolutas y relativas, estableciendo fortalezas o debilidades en relación con la equidad de género. Resultados: Las fortalezas detectadas fueron la valoración de la maternidad/paternidad, la apreciación igualitaria de las opiniones en el ámbito académico y la ausencia de expresiones sexistas en el material docente, entre otras. Como debilidades se mencionaron el trato diferente a hombres y mujeres, la tendencia a minimizar las conductas machistas o sexistas, y la existencia de algunos estereotipos de género. Como propuestas de mejora se propone el diseño de un sistema de reporte interno y capacitación. Conclusiones: Se percibe un ambiente respetuoso en las interacciones académicas. Sin embargo, persisten ciertas expresiones o conductas sexistas, en el trato cotidiano o coloquial, que constituyen aspectos de intervención.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Survey of radiology students' perceptions of gender bias in their training at a Chilean university; medical-education equity rather than the research workforce.
It studies gender bias in radiology training, not the research workforce or research practice.
Gender bias in clinical radiology training is medical education equity, not research workforce or research practice.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".