Brain and behavior in health communication: The Canadian COVID-19 Experiences Project
Bibliographic record
Abstract
Background: Vaccine hesitancy and inconsistent mitigation behavior performance have been significant challenges throughout the COVID-19 pandemic. In Canada, despite relatively high vaccine availability and uptake, willingness to accept booster shots and maintain mitigation behaviors in the post-acute phase of COVID-19 remain uncertain. The aim of the Canadian COVID-19 Experiences Project (CCEP) is threefold: 1) to identify social-cognitive and neurocognitive predictors of mitigation behaviors, 2) to identify optimal communication strategies to promote vaccination and mitigation behaviors, and 3) to examine brain health outcomes of SARS-CoV-2 infection and examine their longevity. Methods: The CCEP is comprised of two components: a conventional population survey (Study 1) and a functionally interconnected laboratory study (Study 2). Study 1 will involve 6 waves of data collection. Wave 1, completed between 28 September and 21 October 2021, recruited 1,958 vaccine-hesitant (49.8%) and fully vaccinated (50.2%) adults using quota sampling to ensure maximum statistical power. Measures included a variety of social cognitive (e.g., beliefs, intentions) and neurocognitive (e.g., delay discounting) measures, followed by an opportunity to view and rate a set of professionally produced COVID-19 public service announcement (PSA) videos for perceived efficacy. Study 2 employs the same survey items and PSAs but coupled with lab-based eye tracking and functional near-infrared spectroscopy (fNIRS) to directly quantify neural indicators of attention capture and self-reflection in a smaller community sample. In the final phase of the project, subjective impressions and neural indicators of PSA efficacy will be compared and used to inform recommendations for construction of COVID-19 PSAs into the post-acute phase of the pandemic. Discussion: The CCEP provides a framework for evaluating effective COVID-19 communication strategies by levering conventional population surveys and the latest eye-tracking and brain imaging metrics. The CCEP will also yield important information about the brain health impacts of SARS-CoV-2 in the general population, in relation to current and future virus variants as they emerge.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".