COVID-19-related lost productivity measured by days missed from work by gender and role among students and employees in Canadian dental faculties
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, the dental workforce, including trainees, was at high risk of infection. Studies suggest that women faced a greater burden due to increased household and work responsibilities. However, few studies have examined work-related absenteeism in dental schools, particularly in relation to gender differences.Objectives: The study objectives were to a) estimate the difference in the proportion of days off work among women and men in a sample of students and staff in Canadian dental schools during the COVID-19 pandemic from April 2021 to April 2022; b) estimate the difference in the proportion of days off work among the students and employees during the same period. Methods: This study used a prospective cohort study database of 10 Canadian dental schools involving 600 participants (students, faculty, and staff) at baseline. Monthly self-reported online questionnaires were collected from April 2021 to April 2022. Data included demographics, work role, province, chronic conditions, COVID-19 infections and symptoms, time off work, vaccination, participation in dental care, exposures with co-workers, and COVID-19-related anxiety. To account for differing follow-up durations, days off work were calculated as a proportion of follow-up days. Descriptive statistics and bivariate tests compared mean proportions of days off work across covariate categories. Negative binomial regression was also used, adjusting for covariates and controlling for follow-up duration with an offset.Results: Participants had a mean age of 36 (SD=14.3) years, 66.8% were women, and 52.5% were students. A total of 44.3% did not complete all follow-up evaluations. Regression analysis showed that women reported 40% higher rates of missed workdays than men (IRRadj=1.4, 95% CI: 0.93–2.07), though this was not statistically significant. Students reported missing 70% fewer workdays than employees (IRRadj=0.3, 95% CI: 0.17–0.50).Conclusions: This prospective cohort study has significant implications for workplace policies. The higher reported absenteeism rates among women suggest a need for gender-sensitive workplace policies. The higher absenteeism among employees may be partially due to attrition as students graduated
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".