Paying the price? Academic work and parenting during COVID-19
Bibliographic record
Abstract
Introduction: The shift to remote working/learning to slow transmission of the SARS-CoV-2 virus has had widespread mental health impacts. We aimed to describe how the COVID-19 pandemic impacted the mental health of students and faculty within a health sciences faculty at a central Canadian university. Methods: Via an online survey, we queried mental health in the first four months of the COVID-19 pandemic quantitatively (scale: 1 (most negative)-100 (most positive)) and qualitatively. Results: The sample (n = 110) was predominantly women (faculty 39/59; [66.1%]; students 46/50; [92.0%]). Most faculty were married/common law (50/60; [84.8%]) and had children at home (36/60; [60.0%]); the opposite was true for most students. Faculty and students self-reported comparable mental health (40.47±24.26 and 37.62±26.13; respectively). Amongst women, those with vs. without children at home, reported significantly worse mental health impacts (31.78±23.68 vs. 44.29±27.98; respectively, p = 0.032). Qualitative themes included: “Sharing resources,” “spending money,” “few changes,” for those without children at home; “working at home can be isolating,” including the subtheme, “balancing act”: “working in isolation,” “working more,” for those with children at home. Discussion: Amongst women in academia, including both students and faculty, those with children at home have disproportionately worse mental health than those without children at home.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".