COVID-19 Vaccine Information Seeking Behaviour in Elderly Punjabi Immigrants in the Greater Toronto Area
Bibliographic record
Abstract
Background: Health disparities among ethnic communities have become more evident during the COVID-19 pandemic and misinformation has become prevalent in media. Health information sources play a key role in addressing health care disparities experienced by immigrants. The objective of this study was to understand elderly Punjabi immigrants’ COVID-19 vaccine-related information-seeking behaviour in the Greater Toronto Area (GTA), Ontario. Method: 341 elderly Punjabi immigrants from the GTA were surveyed about their COVID-19 vaccine information-seeking behaviour, including where they obtain vaccine-related information from, why they sought information from their chosen sources, and barriers to obtaining information. The data collected were analyzed to determine major findings about sources of COVID-19 vaccine information in relation to age, education, and years since immigration. Results: The study included 218 males and 123 females, with a mean age of 67.7 years (standard deviation ± 12.1). 56.6% of participants did not complete high school and 83.9% of participants were retired. Participants’ most preferred source of COVID-19 vaccine-related information was television (25.6%), followed by interpersonal communication with family/friends (22.1%), and social networking sites (11%). Preferred sources and information-seeking behaviour varied with age, education, and years since immigration to Canada. Participants of higher age, lower education, and shorter time since immigration were less likely to use reliable government sources and more likely to use interpersonal communication. Conclusion: In the long term, identified sources of information can be used to educate and share accurate health and wellness information to older Punjabi immigrant populations, ultimately decreasing health disparities and improving health literacy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".