Correlates of hot day air-conditioning use among middle-aged and older adults with chronic heart and lung diseases: the role of health beliefs and cues to action
Bibliographic record
Abstract
Extreme ambient heat is a serious public health threat, especially for the elderly and persons with pre-existing health conditions. Although much of the excess mortality and morbidity associated with extreme heat is preventable, the adoption of effective preventive strategies is limited. The study reported here tested the predictive power of selected components of the Health Belief Model for air-conditioning (AC) use among 238 non-institutionalized middle-aged and older adults with chronic heart failure and/or chronic obstructive pulmonary disease living in Montréal, Canada. Respondents were recruited through clinics (response rate 71%) and interviews were conducted in their homes or by telephone. Results showed that 73% of participants reported having a home air conditioner. The average number of hours spent per 24-hour period in air-conditioned spaces during heat waves was 14.5 hours (SD = 9.4). Exploratory structural equation modeling showed that specific beliefs about the benefits of and drawbacks to AC as well as internal cues to action were predictive of its level of use, whereas the perceived severity of the effects of heat on health was not. The findings are discussed in light of the need to adequately support effective response to extreme heat in this vulnerable population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".