Career Development of Working Mothers: Helping and Hindering Factors in Doing Well in light of the COVID-19 Pandemic
Bibliographic record
Abstract
Women’s career development amid the COVID-19 pandemic suggests that we may be facing a ‘female recession’, where women are at significantly increased risk for dropping out of the workforce with the gender gap in the workplace likely to grow. However, the pandemic may have presented opportunities for working mothers to engage creatively in personal career decisions due to increased opportunities to work flexibly and pivot in a very quickly changing labour market. This qualitative study used the enhanced critical incident technique to explore the intersection of working mothers and career development considering the COVID-19 pandemic. Participants for this study were a sample of 18 working mothers in North America and Australia. Key factors that were identified as helping women do well in their career development during COVID-19 included: Supportive workplaces, social support, personal protective factors, job market factors, and resources (predominantly financial). Hindering factors to working mothers’ career development included: workplace challenges, family challenges, personal stressors, job market factors, COVID-19 mandates and restrictions, and childcare. The findings from this study help elucidate factors that contribute to a meaningful and productive career so that clinicians and other professionals can support, advocate, and encourage women who remain working during motherhood.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".