Addressing Weather-Related Physical and Mental Health Issues
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to enhance a person-centered approach in case management, disability management, and allied disciplines by taking into account the environmental factors affecting people's physical, mental, and emotional health. Drawing from recent research, as well as the example of health support for agriculture producers and workers, the article underscores the weather-related risks that impact people because of where and how they live and work. PRIMARY PRACTICE SETTINGS: The article addresses professional case managers and allied professionals such as disability management specialists in a variety of settings, including acute care, subacute/rehabilitation, workers' compensation, occupational health and safety, primary care, and community-based care. IMPLICATIONS FOR CASE MANAGEMENT: In response to evidence of the rising health risks from weather-related events, professional case managers should expand how they assess individuals (known as "patients" in some settings) to identify their existing and potential risk factors, including where people live and work and the weather-related risks that may be present in those environments. A case management assessment of an individual's weather-related health risks could not only inform a particular treatment episode but also help avoid preventable setbacks and recurrence of illnesses. If those risks are not addressed, they could lead to costly emergency department visits and/or hospitalization.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".