COVID-19 Vaccine Communications on Instagram and Vaccine Uptake in Young Adults: A Content Assessment and Public Engagement Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Canada, during the pandemic, young adults (18–29 year-olds) represented one of the least-vaccinated age groups against COVID-19. These low vaccination rates, and high infection rates, left young people vulnerable to severe infections and created a risk for transmission to immunocompromised populations. Given young adults’ unique characteristics, to encourage vaccination among this demographic, public health and government officials must adopt an audience-centred approach to communication. We sought to understand if the vaccine messages from 8 Canadian federal government Instagram accounts met the needs of young adults based two frameworks: Guiding Principles for Crisis Communication (compassion and empathy, conversational tone, transparency, clarity, call to action and correction of misinformation), and the 5C Model for Vaccine Hesitancy (confidence, complacency, constraints, collective responsibility, and risk calculation). Across 159 posts that mentioned COVID-19 vaccines, there was limited incorporation of best practices, suggesting the government’s communication strategy did not meet the needs of young people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it