Applying a diffusion of innovations framework to characterise diffusion groups and more effectively reach late adopters: a cross-sectional study on COVID-19 vaccinations in Canada in late 2021
Bibliographic record
Abstract
Background: Rogers' diffusion of innovation theory suggests innovations are adopted in stages by different groups (innovators/early adopters, early majority, late majority and late adopters). In healthcare, this could mean that there is the potential to worsen health disparities as later groups tend to also face more social and structural barriers. Determining the unique sociodemographic characteristics, beliefs and attitudes of those in each diffusion category could be useful for theorising how to reach later groups more effectively. Methods: Using a cross-sectional survey among Canadian adults in late 2021, we assigned respondents to diffusion groups based on when they received their first dose, relative to others within their age group in accordance with Rogers' model (ie, cut points: 16%, 50%, 84% with 100% being all those vaccinated within the age group). Participants answered questions about their COVID-19 vaccinations and questions related to their motivations, beliefs, values and attitudes towards COVID-19. A multinomial logistic regression model assessed the likelihood of participants being associated with each diffusion category (with the significance level set at p<0.05). Results: The final sample included 2131 respondents. Late adopters were significantly more likely to identify as non-white, live in rural locations and receive vaccinations at pharmacies. Innovators and early adopters were significantly more likely to get vaccinated in settings other than pharmacies or community centres. Conclusion: A diffusion group-based analysis brought insight into how vaccination strategies could be tailored to reach each diffusion group sooner, particularly late adopters who encounter more barriers.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".