Supporting data for: Assessing knowledge, attitudes, willingness, and barriers to pneumococcal vaccination among Canadian older adults: a cross-sectional survey
Bibliographic record
Abstract
Pneumococcal disease is a leading cause of morbidity and mortality worldwide, with older adults aged 65 and above at particularly high risk for invasive pneumococcal infections. In Canada, pneumococcal vaccination has been recommended for this age group since 1989, yet coverage remains below national targets. Currently, only about 55% of older adults report being vaccinated, falling short of the 80% target. This study assessed knowledge, attitudes, willingness, and barriers to pneumococcal vaccination among unvaccinated older adults. We used baseline data from a randomized controlled trial conducted as a cross-sectional survey among community-dwelling adults aged 65 years and older, residing in any of the ten Canadian provinces, and who self-identified as unvaccinated against pneumococcal disease. The survey was administered online using a tailored web-based electronic data capture system. Data were collected between June 20, 2024, and December 12, 2024, capturing data on participants’ knowledge, attitudes, and willingness to receive the vaccine, along with perceived barriers. Ordinal logistic regression was used to identify factors associated with willingness to be vaccinated, categorized as “willing”, “not willing”, and “I don’t know”. Data Access Note: The anonymized study datasets are not included in this metadata submission. Access may be granted upon request to the study team for appropriate research or verification purposes. Depending on the intended use, external researchers may need approval from their own institutional research ethics board, and a confidentiality agreement may be required. Requests can be directed to: Giorgia Sulis at gsulis@uottawa.ca
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 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 | 0.020 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.403 | 0.054 |
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".