A Missed Opportunity: Evaluating Immunization Status and Barriers in Hospitalized Children
Bibliographic record
Abstract
OBJECTIVE: Hospitalized children are a potentially underimmunized population. We sought to determine the proportion of patients admitted to our pediatric medicine inpatient units who are underimmunized or unimmunized and to identify barriers to immunization faced by families of children admitted to hospital. METHODS: We conducted a prospective study of children aged 2 months to 18 years admitted to our pediatric medicine inpatient units between July 2021 and October 2022. Immunization and demographic data were collected from electronic medical charts. Immunization status of each child was categorized as up-to-date if they had received all eligible vaccine doses in accordance with the provincial immunization schedule. Caregivers completed a survey on barriers to immunizations; results were compared between caregivers of children whose vaccines were up-to-date and those who were not. RESULTS: Hospitalized children were missing more doses of the preschool vaccines than the general population based on published provincial data. Only 142 of 244 (58.2%) of study patients were up-to-date on all their immunizations. Caregivers of children whose immunizations were not up-to-date reported significantly more barriers to vaccination in all survey categories: access to shots, concerns about shots, and importance of shots. CONCLUSIONS: There is a disparity in immunization status between children admitted to hospital in a Canadian setting compared with national targets and community immunization rates. Caregivers of underimmunized hospitalized children cited significantly more barriers to immunization when compared with hospitalized children who are up-to-date. Pursuing a hospital-based immunization strategy could lead to improved immunization status for hospitalized children.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".