Determinants of non-vaccination against seasonal influenza during pregnancy
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to identify the determinants of influenza non-vaccination during pregnancy in Canada. METHODS: Biological mothers of children born between December 2018 and March 2019 were surveyed about vaccinations they had received during pregnancy, reasons for non-vaccination, obstetrical history, and demographics. Simple and multiple logistic regression models were used to measure associations between various sociodemographic factors as well as obstetrical history, and non-vaccination against influenza. We analyzed data from 2361 mothers. RESULTS: Factors associated with non-vaccination included being followed during pregnancy by a midwife compared to by an obstetrician-gynecologist (OR 2.02; 95% CI, 1.17‒3.50); having two or more past live births compared to none (OR 1.58; 95% CI, 1.01‒2.49); having an education level below high school diploma compared to a bachelor's degree or above (OR 2.50; 95% CI, 1.06‒5.90); and having a household income below $60,000 (OR 2.46; 95% CI, 1.42‒4.24) or between $60,000 and $99,999 (OR 2.77; 95% CI, 1.70‒4.52) compared to a household income of $140,000 or more. The province or territory of prenatal care proved to be an important factor in non-vaccination, with statistically significant odds ratios for certain provinces: OR 7.50 (95% CI, 1.40‒40.26) for Ontario, 8.23 (95% CI, 1.53‒44.23) for Newfoundland and Labrador, and 11.39 (95% CI, 2.14‒60.60) for Quebec, as compared to the territories. CONCLUSION: Despite universal access to influenza vaccines in Canada during pregnancy, regional variations and socioeconomic disparities in non-vaccination are still observable.
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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 | 0.003 |
| 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) | 0.002 | 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".