Health Literacy and Health Care System Confidence as Determinants of Attitudes to Vaccines in France: Representative Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Health literacy involves individuals' knowledge, personal skills, and confidence to take action to evaluate and appraise health-related information and improve their health or that of their community. OBJECTIVE: This study aimed to analyze the association between health literacy and attitude toward vaccines, adjusted with other factors. METHODS: We used the SLAVACO Wave 3, a survey conducted in December 2021 among a sample of 2022 individuals, representative of the French adult population. We investigated factors associated with the attitude toward vaccines using respondents' different sociodemographic data, health literacy levels, and the health care system confidence levels using a multinomial logistic regression analysis. RESULTS: Among the participants, 440.4 (21.8%) were classified as "distrustful of vaccines in general," 729.2 (36.1%) were "selectively hesitant," and 852.4 (42.2%) were "nonhesitant." In our model, the level of health literacy was not statistically different between the "distrustful of vaccines in general" and the "selectively hesitant" (P=.48), but it was associated with being a "nonhesitant" (adjusted odds ratio [aOR] 1.86, 95% CI 1.25-2.76). The confidence in the health care system was a strong predictor for a "nonhesitant" attitude toward vaccines (aOR 12.4, 95% CI 7.97-19.2). We found a positive correlation of 0.34 (P<.001) between health literacy and confidence in the health care system, but the interaction term between health literacy and health care system confidence was not significant in our model. CONCLUSIONS: Health literacy was associated with a "nonhesitant" attitude toward vaccines. The findings demonstrated that health literacy and confidence in the health care system are modestly correlated. Therefore, to tackle the subject of vaccine hesitancy, the main focus should be on increasing the population's confidence and on increasing their health literacy levels or providing vaccine information addressing the needs of less literate citizens.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".