Integrating gender in medical curriculum of Bangladesh: exploring perceptions, prospects and challenges
Bibliographic record
Abstract
Abstract Background Gender is an important biological, behavioral, societal and cultural factor influencing affecting health and health care utilization. In medical education, gender tends to be less prioritized and limiting gender sensitivity among medical professionals leading to compromised and substandard health care. Our study aims to investigate the gender perceptions among medical students and practitioners identifying possibilities and challenges in better integrating gender into medical curriculum. Method Quantitative data were collected from 249 respondents (151 medical students, 33 service providers and 65 service recipients) by using structured questionnaires. Descriptive and univariate analysis were conducted to assess socio-demographic characteristics and gender perceptions of participants respectively. To determine the relations of mean perception score with socio-demographic variables, we used one-way ANOVA tests. Finally, we performed multivariate linear regression to determine socio-demographic variables predicting perceptions of respondents towards gender. SPSS version 25 was used for analysis. For qualitative data 16 key informants (6 administrative staffs, 2 policy makers and 8 teaching staffs) were interviewed. The interviews were analyzed manually using thematic analysis procedure. Result Mean score of perception on ‘gender’ among medical students and medical professionals were 10.29 (SD = 2.70) with 52% positive perception and 9.94 (SD = 2.98) with 50% positive perception out of 20 respectively. Significantly greater perception was found among female compared to male. Mean perception score was found significantly higher among respondents aged 20–25 years and students studying in Government medical college. In terms of opinion regarding gender integration in medical or dental curriculum, maximum respondents (91%) thought that inclusion may initiate gender sensitive attitude and respectful behavior and 85% respondents thought people’s health care rights will be ensured. Regarding challenges of integrating greater gender content in medical curriculum, majority service providers (42%) said there are no challenges, but 70% of students responded that due to the huge syllabus, it may create an extra burden to students. The majority of respondents recommended to start reviewing curriculum by a review board (91%) and to develop an intention module (85%). Qualitative findings supported the quantitative results. Conclusion An early sensitization on gender among medical personnel and it’s influence on health care system could contribute in ensuring gender equitable health services and achieving SDGs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".