Gender-based disparities on health indices during COVID-19 crisis: a nationwide cross-sectional study in Jordan
Bibliographic record
Abstract
BACKGROUND: COVID-19 has an inevitable burden on public health, potentially widening the gender gap in healthcare and the economy. We aimed to assess gender-based desparities during COVID-19 in Jordan in terms of health indices, mental well-being and economic burden. METHODS: A nationally representative sample of 1300 participants ≥18 years living in Jordan were selected using stratified random sampling. Data were collected via telephone interviews in this cross-sectional study. Chi-square was used to test age and gender differences according to demographics, economic burden, and health indices (access to healthcare, health insurance, antenatal and reproductive services). A multivariable logistic regression analysis was used to estimate the beta-coefficient (β) and 95% confidence interval (CI) of factors correlated with mental well-being, assessed by patients' health questionnaire 4 (PHQ-4). RESULTS: 656 (50.5%) men and 644 (49.5%) women completed the interview. Three-fourths of the participants had health insurance during the COVID-19 crisis. There was no significant difference in healthcare coverage or access between women and men (p > 0.05). Half of pregnant women were unable to access antenatal care. Gender was a significant predictor of higher PHQ-4 scores (women vs. men: β: 0.88, 95% CI: 0.54-1.22). Among women, age ≥ 60 years and being married were associated with significantly lower PHQ-4 scores. Only 0.38% of the overall participants lost their jobs; however, 8.3% reported a reduced payment. More women (13.89%) were not paid during the crisis as compared with men (6.92%) (P = 0.01). CONCLUSIONS: Our results showed no gender differences in healthcare coverage or access during the COVID-19 crisis generally. Women in Jordan are experiencing worse outcomes in terms of mental well-being and economic burden. Policymakers should give priority to women's mental health and antenatal and reproductive services. Financial security should be addressed in all Jordanian COVID-19 national plans because the crisis appears widening the gender gap in the economy.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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 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".