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Record W4403074726 · doi:10.2147/amep.s493686

Key Learnings and Perspectives of a Newly Implemented Sex-and Gender-Based Medicine Modular Course Integrated into the First-Year Medical School Curriculum: A Mixed-Method Survey [Response To Letter]

2024· letter· en· W4403074726 on OpenAlexaff
Nicola Luigi Bragazzi, Hicham Khabbache, Khalid Ouazizi, Driss Ait Ali, Hanane El Ghouat, Laïla El Alami, Hisham Atwan, Ruba Tuma, Nomy Dickman, Raymond Farah, Rola Khamisy‐Farah

Bibliographic record

VenueAdvances in Medical Education and Practice · 2024
Typeletter
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsYork University
Fundersnot available
KeywordsCurriculumKey (lock)Modular designMedical educationMathematics educationComputer sciencePsychologyMedicinePedagogyProgramming language

Abstract

fetched live from OpenAlex

Thank you for your thoughtful response 1 to our study, "Key Learnings and Perspectives of a Newly Implemented Sexand Gender-Based Medicine Modular Course Integrated into the First-Year Medical School Curriculum". 2 We are delighted to hear that our efforts to integrate Sex and Gender-Based Medicine (SGBM) into medical education were appreciated and recognized for their contribution to advancing personalized and equitable healthcare.We greatly value the additional insights you have shared, particularly regarding the need for a more gender-inclusive framework that encompasses the experiences of all sexes and genders, including non-binary and transgender individuals.This is a critical area for future course development, and your suggestions emphasize the importance of expanding our curriculum to ensure inclusivity for all.Regarding the concerns about the perceived marginalization of male participants and the strong focus on female health, we acknowledge the need for a balanced representation of gender perspectives.Your feedback, along with that of our students, will be instrumental as we work to improve inclusivity and ensure that the curriculum addresses a comprehensive understanding of gender-sensitive healthcare practices.We also take seriously your recommendation to incorporate more peer-reviewed research and empirical evidence.Strengthening the scientific foundation of the course is a top priority, and we will explore ways to integrate more robust data on gender disparities and the healthcare experiences of diverse populations.Your point about the demanding nature of the seven 90-minute sessions for first-year students is also well-taken.We will review the course structure to find a balance that allows for an in-depth exploration of key topics without overwhelming students.It is worth noting, however, that our course includes highly interactive teaching modules.These sessions involve students working in small groups of 10-11, with two tutors per group, providing an engaging, hands-on learning experience to reinforce the content covered in the lectures.

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.

How this classification was reachedexpand

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)0.013
metaresearch head score (Gemma)0.113
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.113
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.037
GPT teacher head0.449
Teacher spread0.413 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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