Perspectives of Older Adults on COVID-19 and Influenza Vaccination in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVES: Addressing vaccine hesitancy has become an increasingly important public health priority in recent years. There is a paucity of studies that have focused on vaccine hesitancy among older adults, who are known to be at greater risk of complications from infections such as COVID-19. We aim to explore the attitudes and beliefs of older adults regarding COVID-19 and influenza vaccines in Toronto, Ontario. METHODS: Older adults enrolled in the Student Senior Isolation Prevention Partnership (SSIPP) program at the University of Toronto were contacted to participate in a phone survey and semi-structured interview. Survey data was analyzed descriptively, and attitude toward vaccination was compared between sociodemographic groups by using Fisher's exact test. Interview audio files were transcribed verbatim and analyzed inductively for themes and sub-themes. RESULTS: All thirty-three (100%) older adults reported that they had received the first and second doses of the COVID-19 vaccine. Twenty-six (78.8%) participants reported intent to get vaccinated against influenza or had already received the influenza vaccine that year. Notably, only 2 out 7 (28.6%) individuals who did not plan to get vaccinated against influenza believed that vaccines offered by health providers are beneficial and only 3 out of 7 (42.9%) agreed that getting vaccines is a good way to protect oneself from disease. No other significant differences in attitudes among participants were found when compared by gender, ethnicity, or education level. The qualitative data analysis of interview transcripts identified 5 themes that impact vaccine decision making: safety, trust, mistrust, healthcare experience, and information dissemination and education. CONCLUSIONS: Our data showed that older adults in the SSIPP program generally had positive views toward vaccination, especially toward the COVID-19 vaccines. However, several concerns regarding the effectiveness of the vaccines were brought up in interviews, such as the speed at which the vaccines were produced and the inconsistency in government messaging.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".