A mixed methods study of seasonal influenza vaccine hesitancy in adults with chronic respiratory conditions
Bibliographic record
Abstract
BACKGROUND: Seasonal influenza vaccination is recommended for patients with chronic respiratory conditions, but uptake is suboptimal. We undertook a comprehensive mixed methods study in order to examine the barriers and enablers to influenza vaccination in patients with chronic respiratory conditions. METHODS: Mixed methods including a survey (n = 429) which assessed sociodemographics and the psychological factors associated with vaccine uptake (ie confidence, complacency, constraints, calculation and collective responsibility) with binary logistic regression analysis. We also undertook focus groups and interviews (n = 59) to further explore barriers and enablers to uptake using thematic analysis. RESULTS: The survey analysis identified that older participants were more likely to accept the vaccine, as were those with higher perceptions of collective responsibility around vaccination, lower levels of complacency and lower levels of constraints. Thematic analysis showed that concerns over vaccine side effects, lack of tailored information and knowledge, and a lack of trust and rapport with healthcare professionals were key barriers. In contrast, the importance of feeling protected, acceptance of being part of an at-risk group and feeling a reduced sense of vulnerability after vaccination were seen as key enablers. CONCLUSIONS: Our findings showed that the decision to accept a vaccine against influenza is influenced by multiple sociodemographic and psychological factors. Future interventions should provide clear and transparent information about side effects and be tailored to patients with chronic respiratory conditions. Interactions between patients and their healthcare providers have a particularly important role to play in helping patients address their concerns and feel confident in vaccination.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".