Air quality and health education to increase knowledge and encourage health protective behaviour among older adults in Hamilton, Canada
Bibliographic record
Abstract
Air pollution exposure is detrimental to population health and particularly to older adults (≥65 years of age) who are considered part of the “at-risk” population. The Air Quality Health Index (AQHI) provides air quality and health information such that the public can implement health protective behaviour and decrease exposure to outdoor air pollution. The AQHI education session for older adults aims to increase knowledge, encourage use of the AQHI, and gain a better understanding of how at-risk populations self-identify. An AQHI education session was delivered face-to-face to older adults living independently in Hamilton, Canada. A pre- and post-test questionnaire with both quantitative and qualitative questions was administered to measure knowledge and intention to use AQHI. A total of 62 participants attended the education session and completed the pre- and post-test questionnaire. Results of a paired t test indicated a statistically significant difference in pre- and post-test knowledge (p <0.05). After the education session, 82% of participants indicated their intention to use AQHI. The benefit of using AQHI included health protection while the most relevant barrier was the inability to self-identify as belonging to the elderly at-risk population. An AQHI education session was effective in increasing AQHI knowledge and encouraging use of the AQHI. Consideration must be given to replacing the current terminology “elderly” with the use of chronological age (≥65 years) to describe the at-risk population and foster greater ability to self-identify and use AQHI. Extra attention must be given to engage older adults living in lower socioeconomic areas to address health disparities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".