Moral Hazard Among Health System Workers in the Wake of Climate-Driven Emergency Events
Bibliographic record
Abstract
The Sars-Cov-2 pandemic brought attention to moral hazard among health system workers (HSWs). However, empirical investigations of moral hazard in the context of environmental emergencies, particularly those driven by climate change, are limited. This contribution draws from interviews with 28 HSWs from diverse health service roles across British Columbia, Canada to interrogate how moral hazard may be caused or compounded by climate-related emergency events. Findings suggest three discrete pathways by which moral hazard may manifest among HSWs: ethical trade-offs made by HSWs in caring for themselves, their families, and their neighbours versus the populations they are hired to protect; ethical trade-offs stemming from resource allocation and staffing issues that inhibit quality care provision; and the hierarchical organization of health systems and its relationship to societal structures that contribute to climate change in the first place. The paper discusses findings in relation to the moral hazard literature, and implications for research and practice. Specifically, the authors suggest that enhancing upstream attention to the drivers of climate change and person-centred approaches to management may work to limit moral hazard.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".