A gender breakdown of unexpected benefits generated by work from home in STEM fields − A qualitative analysis of the WiMPBME Task Group survey
Bibliographic record
Abstract
BACKGROUND: Working from home during the Covid-19 pandemic was perceived differently by men and women working in STEM fields. The aim of this paper is to highlight the unexpected benefits generated by working from home during the pandemic. METHODS: Qualitative methodology was used to analyze data, collected via survey. The survey designed and conducted by WiMPBME targeted both males and females working in medical-related STEM fields (physics and engineering) and was answered by 921 individuals from 76 countries across all continents. This report analysed the responses to one open-ended question of the survey, namely: "What is the one positive that you have learnt/experienced as a result of working from home during this pandemic?". RESULTS: 594 responded to the question of interest. Access to home office was reported by 72.2% of survey participants. Males were more likely than females to report no positives of working from home (62.9%). Females were more likely to cite quality time, physical and mental health as positive factors than males, and to mention children in their responses. The most commonly coded thematic unit for males was remote working, with many males reporting the feasibility of working from home. Increased work productivity, better time management and work organisation were other common themes highlighted by responders irrespective of gender. CONCLUSION: The findings of the survey show the diversity of perceptions about remote working in STEM fields, while highlighting the importance of considering family dynamics, individual circumstances as well as gender when evaluating varied experiences of STEM professionals.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".