“I may be essential but someone has to look after my kids”: women physicians and COVID-19
Bibliographic record
Abstract
OBJECTIVES: This paper analyzes results from focus groups held with women physicians in British Columbia which explored questions around how gender norms and roles influenced their experiences during COVID-19. METHODS: Four virtual focus groups were organized between July and September 2020. Participants (n = 27) were voluntarily recruited. Data were analyzed using applied thematic analysis. RESULTS: In addition to the COVID-19-related changes experienced across the profession, women physicians faced distinct challenges related to an increase in unpaid care responsibilities, and often felt excluded from, and occasionally dismissed by, leadership. Women leaders often felt their contributions were unrecognized and undervalued. Participants drew strength from other women leaders, peer networks, and professional support, but these strategies were limited by unpaid care and emotional labour demands, which were identified as increasing risk of burnout. DISCUSSION: Even though women physicians hold a degree of relative privilege, unpaid care work and gender norms contribute to distinct secondary effects of COVID-19. Women physicians link these to pre-pandemic assumptions (within families and communities) that women would absorb care deficits at their own cost. Health system leadership continues to reflect a masculine normative experience wherein the personal and professional are separated, and which devalues the emotional labour often associated with feminine leadership. The strategies participants employed to address negative impacts, while demonstrating resourcefulness and peer support, reflect individualistic responses to social-structural challenges. There is a need for greater recognition of women's contributions at home and work, increased representation in decision-making, and practical supports such as childcare and counselling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".