Mental Health and Other Factors Associated with COVID‐19 Vaccination Intention toward Children of Military Parents in Lambayeque, Peru
Bibliographic record
Abstract
There is evidence that vaccine acceptability is strongly associated with mental health. However, no studies assessing intention to vaccinate (ITV) intention toward children of military parents have been documented. The current research aimed to establish the prevalence and factors of ITV children against COVID-19 in military parents in Lambayeque-Peru, 2021. Analysis was conducted with the dependent variable ITV children reported by military parents. The independent variables were history of mental health, searching for mental health support, food insecurity, resilience, anxiety, depression, burnout, posttraumatic stress, and suicidal risk. Prevalence ratios and 95% confidence intervals were estimated. Of 201 military personnel evaluated, 92.5% were male, 82.5% were of the Catholic faith, and the median age was 40.9% of respondents reported seeking mental health help during the COVID-19 pandemic. It was reported anxiety (20.3%), depression (6.5%), and posttraumatic stress disorder (6.5%). Most reported ITV in children against COVID-19 (93%). In the multiple models, we found that Catholics had a 23% higher prevalence of ITV in the children where PR = prevalence ratios and CI = confidence intervals (PR = 1.23; 95% CI: 1.01-1.50). Likewise, seeking mental health support increased the prevalence of ITV by 8% (PR = 1.08; 95% CI: 1.00-1.15). Seeking mental health support and belonging to the Catholic faith had a higher ITV of children of Peruvian military personnel. Finding mental health support, experiencing burnout syndrome, having a relative who suffers from mental health problems, and being part of the Catholic religion were associated with a higher willingness to immunize the children of Peruvian military members.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".