Factors influencing health care and service providers’ and their respective “at risk” populations’ adoption of the Air Quality Health Index (AQHI): a qualitative study
Bibliographic record
Abstract
BACKGROUND: The Air Quality Health Index (AQHI) provides air quality and health information such that the public can implement health protective behaviours (reducing and/or rescheduling outdoor activity) and decrease exposure to outdoor air pollution. The AQHI's health messages account for increased risk associated with "at risk" populations (i.e. young children, elderly and those with pre-existing respiratory and/or cardiovascular conditions) who rely on health care and service providers for guidance. Using Rogers' Diffusion of Innovations theory, our objective with respect to health care and service providers and their respective "at risk" populations was to explore: 1) level of AQHI knowledge; 2) factors influencing AQHI adoption and; 3) strategies that may increase uptake of AQHI, according to city divisions and socioeconomic status (SES). METHODS: Semi-structured face-to-face interviews with health care (Registered Nurses and Certified Respiratory Educators) and service providers (Registered Early Childhood Educators) and focus groups with their respective "at risk" populations explored barriers and facilitators to AQHI adoption. Participants were selected using purposive sampling. Each transcript was analyzed using an Interpretive Description approach to identify themes. Analyses were informed by Rogers' Diffusion of Innovations theory. RESULTS: Fifty participants (6 health care and service providers, 16 parents, 13 elderly, 15 people with existing respiratory conditions) contributed to this study. AQHI knowledge, AQHI characteristics and perceptions of air quality and health influenced AQHI adoption. AQHI knowledge centred on numerical reliance and health protective intent but varied with SES. More emphasis on AQHI relevance with respect to health benefits was required to stress relative advantage over other indices and reduce index confusion. AQHI reporting at a neighbourhood scale was recognized as addressing geographic variability and uncertainty in perceived versus measured air quality impacting health. Participants predominantly expressed that they relied on sensory cues (i.e. feel, sight, taste) to determine when to implement health protective behaviours. Time constraints were identified as barriers; whereas local media reporting and wearable devices were identified as facilitators to AQHI adoption. CONCLUSION: Increasing knowledge, emphasizing relevance, and reporting AQHI information at a neighbourhood scale via local media sources and wearable devices may facilitate AQHI adoption while accounting for SES differences.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".