Are people interested in receiving advice from their general practitioner on how to protect their health during heatwaves? A survey of the German population
Bibliographic record
Abstract
OBJECTIVE: Climate change increases the frequency, intensity and length of heatwaves, which puts a particular strain on the health of vulnerable population groups. General practitioners (GPs) could reach these people and provide advice on protective health behaviour against heat. Data is lacking on whether and what topic of GP advice people are interested in, and whether specific person characteristics are associated with such interests. DESIGN: Cross-sectional, nationwide, face-to-face household survey, conducted during winter 2022/2023. SETTING: Germany. PARTICIPANTS: Population-based sample of 4212 respondents (aged 14-96 years), selected by using multistratified random sampling (50%) combined with multiquota sampling (50%). MAIN OUTCOME MEASURE: Interest in receiving GP advice on health protection during heatwaves (yes/no), and the topic people find most important (advice on drinking behaviour, nutrition, cooling, cooling rooms, physical activity or medication management). Associations between predefined person characteristics and the likelihood of interest were estimated using adjusted logistic regressions. RESULTS: A total of 4020 respondents had GP contact and provided data on the outcome measure. Of these, 23% (95% CI=22% to 25%) expressed interest in GP advice. The likelihood of expressing interest was positively associated with being female, older age (particularly those aged 75+ years: 38% were interested), having a lower level of educational attainment, having a migration background, living in a more urban area, and living in a single-person household. It was negatively associated with increasing income. Advice on medication management received highest interest (25%). CONCLUSIONS: During winter season 2022/2023, around one quarter of the German population with GP contact-and around 40% of those aged 75+ years-was estimated to have a stated interest in receiving GP advice on protective health behaviour during heatwaves, especially on medication management. Climate change is creating new demands for healthcare provision in general practice. This study provides initial relevant information for research and practice aiming to address these demands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".