Explanations for Not Receiving the Seasonal Influenza Vaccine: An Ontario Canada Based Survey
Bibliographic record
Abstract
Despite evidence of the importance of the seasonal influenza vaccine for both individual and population health, only a third of the Ontario population received the vaccine in 2013/2014. The objective of this study was to identify why Ontarians are not getting the seasonal influenza vaccine. Written responses to the question "Why didn't you get the seasonal flu vaccine in the last flu season?" were deductively analyzed using the Conceptual Model of Vaccine Hesitancy. Inductive coding was also conducted to identify explanations that fall outside of the present model and may be unique to the seasonal influenza vaccine. Data were collected between August and early September, 2014 through a survey in the Region of Waterloo, Ontario. Overall, 91.4% of responses could be explained using the conceptual model and specifically relate to perceived importance of vaccination (46.8%), moral convictions (19.4%), and past experiences with vaccinations services (14.5%). Notably, explanations related to healthcare professional attitudes, risk perceptions and trust, and subjective norms were identified to a much lesser extent than those discussed above. The remaining 8.6% of responses cannot be explained by the model because they do not relate to hesitancy. Our data contribute to the minimal body of Canadian research investigating low uptake of the seasonal flu vaccine, adding to an evidence-base upon which to inform promotional campaigns. Our data also highlight the utility of the Conceptual Model of Vaccine Hesitancy for the design and analysis of research investigating seasonal flu vaccine refusal or delay.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| 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 | 0.000 |
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".