A reflexive thematic analysis exploring the experiences of undergraduate women in STEM in Bangladesh
Bibliographic record
Abstract
Abstract Globally, a significant gender gap is reported in the enrolment of women in Science, Technology, Engineering, and Mathematics (STEM). Bangladesh reports one of the lowest female stakeholder percentages in STEM but has increased demand for skilled STEM professionals. Therefore, this qualitative study explores the experiences of undergraduate women in STEM in Bangladesh. Seven female undergraduate students were recruited using purposeful sampling, and a semi-structured interview was conducted. Reflexive thematic analysis, along with a phenomenological approach, was utilized for data analysis to gain a better understanding of their experiences. The four key themes that emerged were the gendered nature of interactions, the impact of societal barriers, underrepresentation and role models, self-identity, and psychological outcomes. The findings suggested multiple factors like gender-biased interactions in classrooms, lack of access to STEM resources, and lack of female role models negatively impacted students’ academic experiences. Moreover, poor self-esteem in female students contributed to imposter syndrome and heightened career anxiety.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".