The French Covid-19 vaccination policy did not solve vaccination inequities: a nationwide longitudinal study on 64.5 million individuals
Bibliographic record
Abstract
Abstract Context To encourage Covid-19 vaccination, France introduced during the Summer 2021 a “Sanitary Pass,” which morphed into a “Vaccine Passe” in early 2022. While the Sanity 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 Pass’ 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 being a district above the median value of the first-dose vaccination rates and being above the median value of each indicator at different time points: just before the sanitary pass announcement (week 2021-W27), just before the sanitary pass came into force (week 2021-W31) and one month after (week 2021-W35), and the equivalent dates for the vaccine pass (weeks 2021-W49, 2022-W03, 2022-W07). We then considered the change over time of vaccination rates according to deciles of the three of the most associated indicators. Results The indicators most associated with vaccination rates were the share of local income coming from unemployment benefits, the proportion of overcrowded households, the proportion of immigrants in the district, and vote for an “anti-establishment” candidate at the 2017 Presidential election. Vaccination rate also were also contrasted along a North-West – South East axis, with lower vaccination coverage in the South-East of France. Conclusion Our analysis reveals that, both before and after the introduction of the French sanitary and vaccination passes, factors with the largest impact are related to poverty, immigration, and trust in the government.
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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 | 0.004 |
| 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 | 0.001 | 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".