Context matters: How to research vaccine attitudes and uptake after the COVID-19 crisis
Bibliographic record
Abstract
The pandemic dramatically accelerated research on vaccine attitudes and uptake, a field which mobilizes researchers from the social sciences and humanities as well as biomedical and public health disciplines. The field has the potential to contribute much more, but the growth in research and the deeper connections between disciplines brings challenges as well as opportunities. This perspective article assesses the recent development of the field, exploring progress whilst emphasizing that not enough attention has been paid to national and local contexts. This lack of contextual attention limits the progress of research and hinders our capacity to learn from the COVID-19 crisis. We suggest three concrete responses: building and recognizing new publishing formats for reporting and synthesizing studies at a country level; establishing country-level interdisciplinary networks to connect research and praxis; and strengthening international comparative survey work by enhancing the focus on local contextual factors.
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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 | 0.150 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".