FACTORS INFLUENCING AIR QUALITY HEALTH INDEX ADOPTION
Bibliographic record
Abstract
The Air Quality Health Index (AQHI) is a 10-point scale that communicates the cumulative health risks associated with air pollution (ECCC, 2016). The general theme of this dissertation centers on an understanding of AQHI adoption while accounting for socioeconomic status (SES) in order to facilitate AQHI uptake by the public with particular focus on “at risk” populations (i.e. young children, seniors, and those with pre-existing respiratory and/or cardiovascular conditions). The study is unique since it approaches AQHI adoption consistent with the ecological model and an equity lens, and AQHI adoption is considered at the individual, organizational and community levels. The study area for this dissertation is Hamilton, Ontario, Canada. The findings from this dissertation contribute to an understanding of why AQHI is or is not being adopted and suggests potential intervention strategies to increase its uptake. Consistent with health behaviour theory, demographics (gender, age, education, area of residence), knowledge/understanding and individual risk perceptions (neighbourhood air effects on health) were found to be significant predictors of AQHI adoption. Additionally, perceived benefits of AQHI adoption included protection of health for self and those cared for via familial and/or occupational duties. While perceived barriers of AQHI adoption included lack of time required to check and follow AQHI health messages and the inability to “self-identify” as belonging to the “at risk” population. This dissertation proposes that increases in AQHI adoption may be achieved by increasing AQHI knowledge and emphasizing the benefits and relevance of AQHI such that “at risk” populations can self-identify. Additionally, AQHI uptake may be increased by providing AQHI information at a neighbourhood scale via local media sources and wearable devices.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".