The French Covid-19 vaccination policy did not solve vaccination inequities: a nationwide study on 64.5 million people
Bibliographic record
Abstract
BACKGROUND: To encourage Covid-19 vaccination, France introduced during the Summer 2021 a 'Sanitary Pass', which morphed into a 'Vaccine Pass' in early 2022. While the sanitary pass led to an increase in Covid-19 vaccination rates, spatial heterogeneities in vaccination rates remained. To identify potential determinants of these heterogeneities and evaluate the French sanitary and vaccine passes' efficacies in reducing them, we used a data-driven approach on exhaustive nationwide data, gathering 141 socio-economic, political and geographic indicators. METHODS: We considered the association between vaccination rates and each indicator at different time points: before the sanitary pass announcement (week 2021-W27), before the sanitary pass came into force (week 2021-W31) and 1 month after (week 2021-W35) and the equivalent dates for the vaccine pass (weeks 2021-W49, 2022-W03 and 2022-W07). RESULTS: The indicators most associated with vaccination rates were the share of local income coming from unemployment benefits, overcrowded households rate, immigrants rate and vote for an 'anti-establishment' candidate at the 2017 Presidential election. These associations increase over time. Consequently, living in a district below the median of such indicator decreases the probability to be vaccinated by about 30% at the end of the studied period, and this probability gradually decreases by deciles of these indicators. CONCLUSIONS: Our analysis reveals that factors related to poverty, immigration and trust in the government are strong determinants of vaccination rate, and that vaccination inequities tended to increase after the introduction of the French sanitary and vaccination passes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".