Understanding and identifying the cultural predictors of COVID-19 related healthcare behaviours across Canadian and Colombian populations
Bibliographic record
Abstract
Among the most widely used frameworks for examining individual differences among cultures is understanding how individuals define themselves and their relationship with others in the context of the group to which they belong. In more individualistic cultures the core of an individual’s self-definition is rooted in autonomy and separation from others. In contrast, in more collectivistic cultures a person’s self-definition is more embedded in their relationship with others and their interdependence with others in their own social group. (Brewer et al, 2007). Collectivistic and Individualistic cultures have been found to impact health decisions. For instance, people who belong to collectivistic cultures have been found to show better COVID-19 health outcomes (e.g., fewer infections) compared to those from individualistic cultures (Cao et al, 2020; Rajkumar, 2021). We therefore aim to examine how each of these factors operate within a single study. Specifically, we will compare how adherence to COVID-19 preventive behaviours (wearing a mask, getting vaccinated, social distancing and social isolation) varied between an individualistic (Canada) country and a collectivistic (Colombia) country, how such behaviours were predicted by differences in people’s personal motivations, and how such dynamics varied over time.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".