American Thoracic Society Member Survey on Climate Change and Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The American Thoracic Society (ATS), in collaboration with George Mason University, surveyed a random sample of ATS members to assess their perceptions of, clinical experiences with, and preferred policy responses to climate change. An e-mail containing an invitation from the ATS President and a link to an online survey was sent to 5,500 randomly selected U.S. members; up to four reminder e-mails were sent to nonrespondents. Responses were received from members in 49 states and the District of Columbia (n = 915); the response rate was 17%. Geographic distribution of respondents mirrored that of the sample. Survey estimates' confidence intervals were ±3.5% or smaller. Results indicate that a large majority of ATS members have concluded that climate change is happening (89%), that it is driven by human activity (68%), and that it is relevant to patient care ("a great deal"/"a moderate amount") (65%). A majority of respondents indicated they were already observing health impacts of climate change among their patients, most commonly as increases in chronic disease severity from air pollution (77%), allergic symptoms from exposure to plants or mold (58%), and severe weather injuries (57%). A larger majority anticipated seeing these climate-related health impacts in the next 2 decades. Respondents indicated that physicians and physician organizations should play an active role in educating patients, the public, and policy makers on the human health effects of climate change. Overall, ATS members are observing that human health is already adversely affected by climate change and support responses to address this situation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it