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Record W4413258242 · doi:10.2196/78371

Exploring Gender Perspectives in Medical Education: Latent Semantic Analysis of Israeli First-Year Medical Students’ Reflections

2025· article· en· W4413258242 on OpenAlexaffvenue
Rola Khamisy‐Farah, Raymond Farah, Haneen Jabaly-Habib, Yara Nakhleh Francis, Nicola Luigi Bragazzi

Bibliographic record

VenueJMIR Medical Education · 2025
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsYork University
Fundersnot available
KeywordsThematic analysisEthnic groupContent analysisPsychologyHealth careMedical educationMedicineQualitative researchSociologySocial sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Gender is increasingly recognized as a crucial determinant of health and health care delivery. Integrating gender-sensitive content into medical education is essential for cultivating socially responsive, culturally competent, and clinically effective physicians of the future. However, limited research has examined how medical students conceptualize gender in clinical contexts, particularly through their own reflective narratives. OBJECTIVE: This study explores the thematic landscape of gender-related perceptions among first-year medical students in Israel following a mandatory course in gender medicine. Using latent semantic analysis (LSA), we examined how students reflected on gendered dimensions of health care and how these reflections varied by gender and ethnicity. METHODS: First-year medical students enrolled in the four-year path of medicine in Israel participated in a compulsory gender medicine course and were invited to submit anonymous written reflections. A total of 83 students (n=52, 63%, females; n=31, 37%, males; n=68, 82%, Jewish; and n=15, 18%, Arab) submitted responses, which were preprocessed and analyzed using LSA. The texts were lemmatized and vectorized to construct a term-document matrix, followed by singular value decomposition for dimensionality reduction. Ten latent topics were extracted, and thematic labels were assigned through an inductive, consensus-based coding procedure. Subgroup analyses were conducted by gender and ethnicity. RESULTS: LSA identified 10 distinct topics, accounting for 56.6% of the total variance in the overall sample. The most dominant theme was Gendered Patient-Doctor Interactions (eigenvalue=121.188; 28.1% variance; 527 terms; 75 documents), followed, in terms of variance, by Gender-Specific Diseases and Health Concerns (5.7%) and Cultural and Religious Influences on Health Care (4.3%). Reflections from female students introduced 3 unique themes: Gendered Help-Seeking and Familial Roles (2.8%), Gender and Health Education (2.5%), and Gendered Communication and Advocacy (2.2%). Male students uniquely discussed Perceived Gender Bias in Clinical and Research Settings (3.8%) and the Legal and Ethical Dimensions of Reproductive Health Care (3.3%). Among Jewish students, additional themes included Population-Level Framing of Gendered Conditions (3.7%) and Gendered Youth Expectations (2.1%). Arabic students contributed culturally specific themes, such as Modesty and Cultural Norms (8.6%), Paternal Authority and Structural Discrimination (6.3%), and Reproductive Vulnerability (3.6%). CONCLUSIONS: Thematic patterns in student reflections suggest that gender medicine curricula are effective in fostering critical engagement with diverse gendered realities in clinical care. The emergence of culturally grounded and gender-specific themes underscores the importance of tailoring educational interventions to reflect student diversity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.142
GPT teacher head0.486
Teacher spread0.344 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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