Psychological and situational factors associated with COVID-19 vaccine intention among postpartum women in Pakistan: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Contributing factors to COVID-19 vaccination intention in low-income and middle-income countries have received little attention. This study examined COVID-19-related anxiety and obsessive thoughts and situational factors associated with Pakistani postpartum women's intention to get COVID-19 vaccination. DESIGN: Cross-sectional study administering a survey by a telephone interview format between 15 July and 10 September 2020. SETTING: Four centres of Aga Khan Hospital for Women and Children-Garden, Kharadar, Karimabad and Hyderabad-in Sindh Province, Pakistan. PARTICIPANTS: Women who were enrolled in our longitudinal Pakistani cohort study were approached (n=1395), and 990 women (71%) participated in the survey, of which 941 women who were in their postpartum period were included in the final analysis. PRIMARY OUTCOME MEASURE AND FACTORS: COVID-19 vaccine intention, sociodemographic and COVID-19-related factors, Coronavirus anxiety, obsession with COVID-19 and work and social adjustment were assessed. Multiple multinomial logistic regression analysis was used to identify factors associated with women's intentions. RESULTS: Most women would accept a COVID-19 vaccine for themselves (66.7%). Only 24.4% of women were undecided about vaccination against COVID-19, and a small number of women rejected the COVID-19 vaccine (8.8%). Women with primary education were less likely to take a COVID-19 vaccine willingly than those with higher education. COVID-19 vaccine uncertainty and refusal were predicted by having no experience of COVID-19 infection, childbirth during the pandemic, having no symptoms of Coronavirus anxiety and obsession with COVID-19. Predictors for women's intention to vaccinate themselves and their children against COVID-19 were similar. CONCLUSION: Understanding the factors shaping women's intention to vaccinate themselves or their children would enable evidence-based strategies by healthcare providers to enhance the uptake of the COVID-19 vaccine and achieve herd immunity against Coronavirus.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".