312 The factors influencing covid-19 booster vaccine decisions: a systematic review
Bibliographic record
Abstract
Introduction Little is known about the factors influencing people making decisions about the COVID-19 booster vaccine given the evolution of the COVID-19 virus, the updated vaccines, and easing of public health restrictions Our study aims to: (1) identify factors influencing COVID-19 booster vaccine decisions; and (2) determine the decisional needs of people considering the COVID-19 booster vaccine decision. Methods A systematic review was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Eligible studies were conducted with adults who made the COVID-19 booster vaccine decision for themselves and reported the influencing factors or decisional needs. We included qualitative, quantitative, and mixed methods primary studies. Time was limited from December 2019 to the present with no geographic or language limitations. Five databases were searched: MEDLINE, Embase, Web of Science, PsycINFO and CINAHL. Two reviewers independently conducted the title/abstract and full-text screening using Covidence and extracted data based on the Cochrane checklist including (1) study identification, (2) methods, (3) participant characteristics, (4) data collection tool/measurement instruments, (5) influencing factors, (6) decisional needs. We assessed the quality of the included studies using the Mixed Methods Appraisal Tool. We analyzed the findings descriptively. Results Of 3565 records identified, 306 full-text studies were screened for eligibility and 150 studies were included. Preliminary analysis identified concerns about COVID-19 infection, vaccine effectiveness and safety, and public health recommendations as the most common factors influencing decisions about COVID-19 booster vaccination. Decisional needs were inadequate knowledge, inadequate support/resources, and unclear values limiting quality decisions. Discussion Compared to the COVID-19 primary series, new factors influencing booster vaccines were concerns about COVID-19 infection and vaccination history. There are unmet decisional needs for booster vaccines. Conclusion Interventions to support decision making about COVID-19 booster vaccinations need to address unique decisional needs.
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 | 0.037 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".