Work, Parenting, and Well-being
Bibliographic record
Abstract
Background and Objectives COVID-19 reframed the relationship between work and home and, in general, made both more difficult—especially for parents. We hypothesized that, among neurologists, the effects of the pandemic on productivity and on well-being would be greater on those with children than on those without children and that the effects would be greater on women with children than on men with children. Methods We conducted an international electronic survey launched by the Practice Current section of the American Academy of Neurology. The survey included questions on demographics (self-identified gender, number of children and elderly dependents, childcare support, and country and state when applicable), workflow changes because of COVID-19, impacted domains, and productivity and well-being using the Likert scale. Counts are presented as descriptive statistics. Statistical analysis was performed using Mann-Whitney U and Kruskal-Wallis tests. Results We collected 243 fully completed surveys from providers in all continents with high representation of the United States (76%), providers who identified as women (71.6%), and neurologists with children (91%) among respondents. A majority worked remotely (28% fully, 43% mix). Neurologists reported decreased academic productivity (72%), work benefits (65%), and time for writing (48%). These findings were more prominent in respondents with children and among women practicing outside of the United States. Increased pressure from productivity expectations and lack of time for family were reported by 47% and 41% of respondents, respectively. Discussion The disruption from the COVID-19 pandemic affected academic productivity and decreased the well-being of neurologists in general and of neurologists with children more drastically. This could potentially hinder the promotion and retention of junior neurologists who were juggling life and work during the pandemic outbreak and its recurrent surges.
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
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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 teacher head, 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".