Gender, Growth Mindset, and Covid-19: A Cluster Randomized Controlled Trial in Bangladesh
Bibliographic record
Abstract
School closures during the covid-19 pandemic disrupted learning among students globally, with concerns for long-term impacts on adolescent well-being and likely differential effects for boys versus girls. This study explores the gendered impacts of covid-19-related school closures on continued learning and motivation among secondary-school students in Bangladesh and presents short-term impacts of a cluster randomized intervention that offered students an innovative, virtually-delivered Growth Mindset curriculum. During the covid-19 pandemic, our analysis highlights that boys were significantly more likely to engage with media for continued learning, whereas girls were more likely to use books and paper assignments. Motivation for learning and aspirations for higher education fell during the covid-19 pandemic, particularly for girls. The randomized Growth Mindset intervention, which promoted the idea that individual characteristics, such as intelligence can be developed through practice, results in significant increases in adolescent motivation and aspirations across both genders. For boys, the effect sizes are large enough to compensate for negative covid-19 pandemic impacts; however, due to the larger negative impacts of the pandemic for girls, a covid-19 pandemic-related gender gap persists. Our findings suggest that a virtually-delivered Growth Mindset intervention mitigates the negative impacts of extended school closures, but that additional policies are needed to address gender differences in adolescent outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".