Understanding nurses' experience of climate change and then climate action in Western Canada
Bibliographic record
Abstract
AIM: To understand nurses' personal and professional experiences with the heat dome, drought and forest fires of 2021 and how those events impacted their perspectives on climate action. DESIGN: A naturalistic inquiry using qualitative description. METHOD: Twelve nurses from the interior of British Columbia, Canada, were interviewed using a semi-structured interview guide. Thematic analysis was employed. No patient or public involvement. RESULTS: Data analysis yielded three themes to describe nurses' perspective on climate change: health impacts; climate action and system influences. These experiences contributed to nurses' beliefs about climate change, how to take climate action in their personal lives and their challenges enacting climate action in their workplace settings. CONCLUSIONS: Nurses' challenges with enacting environmentally responsible practices in their workplace highlight the need for engagement throughout institutions in supporting environmentally friendly initiatives. IMPACT: The importance of system-level changes in healthcare institutions for planetary health.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".