COVID-19 Vaccine Hesitancy Among Pediatric Oncology and Bone Marrow Transplant Patients
Bibliographic record
Abstract
Background/Objectives: Vaccine hesitancy among immunocompromised patients is complex and not well understood. This study aimed to determine the rate of COVID-19 vaccine hesitancy among pediatric oncology and bone marrow transplant (BMT) patients and to understand associated factors. Methods: Parents of patients (≤18 years) with cancer or post-BMT completed the Parent Attitudes about Childhood Vaccines Survey. A COVID-19 vaccine hesitancy score (VHS-COVID) was calculated from 0 to 100 (higher scores indicating increasing hesitancy). A small group of patients (patients older than 15 years) were also surveyed directly. Results: Among 113 parent respondents, the majority were female (58%) and at least college/university educated (78%). The majority (73%) of patients had cancer (61% leukemia/lymphoma, 37% solid/CNS tumors), while 27% had received BMT for malignant and non-malignant conditions. Only 48% of patients had been vaccinated against COVID-19, compared to 88% of parents. Ineligibility due to phase of cancer/BMT treatment (27%), vaccine hesitancy (24%), and age (24%) were the top three reasons for not vaccinating against COVID-19. Only 13% of parents said they would “definitely vaccinate” if their child became eligible. VHS-COVID scores were higher for parents of patients in surveillance versus active therapy (mean 61 vs. 48; p = 0.03). Parents who had received fewer COVID-19 vaccine doses (0–1 vs. ≥2) were more hesitant toward all vaccines (p = 0.0002), COVID-19 vaccines (p = 0.0003), and influenza vaccines (p = 0.005). Conclusions: Vaccine hesitancy is common among this population and was demonstrated through beliefs (hesitancy scores) as well as vaccine uptake. Future work should focus on education targeting vaccine eligibility and engaging with vaccine hesitant families in the immunocompromised community.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".