Factors influencing health behaviours in response to the air quality health index: a cross-sectional study in Hamilton, Canada
Bibliographic record
Abstract
Research associating adverse health effects with air pollution exposure is robust. Public health authorities recognize the need to implement population health strategies that protect public health from air pollution exposure. The Air Quality Health Index (AQHI) is a public health initiative that is intended to protect the public's health from exposure to air pollution. The aim of this research was to identify and explain factors influencing AQHI adoption at the individual level and to establish intervention strategies. A cross-sectional survey with both quantitative and qualitative questions was administered in Hamilton, Ontario, Canada, during the months of June to October 2012. Logistic regression and the Health Belief Model are used to explore the data. Demographics (gender, age, education, and area of residence), knowledge/understanding, and individual risk perceptions (neighbourhood air effects on health) were found to be significant predictors of AQHI adoption. The perceived benefits of AQHI adoption included protection of health for self and those cared for via familial and (or) occupational duties, whereas the perceived barriers of AQHI adoption included lack of knowledge about where to check and lack of time required to check and follow AQHI health messages. Also, self-efficacy was uncovered as a factor influencing AQHI adoption. Accordingly, increases in AQHI adoption could be achieved via increasing AQHI knowledge among low socioeconomic status females, communicating the benefits of AQHI adoption to “at-risk” populations and implementing supports for males to follow AQHI health messages.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".