The Environment as a Patient: A Content Analysis of Canadian Nursing Organizations and Regulatory Bodies Policies on Environmental Health
Bibliographic record
Abstract
BACKGROUND: Individual, community, and societal health is impacted by the environment, specifically by air, water and soil pollution, and climate change. Poor environmental conditions have been associated with many illness exacerbations. Although global nursing organizations have increased their environmental health focus, evidence is lacking that Canadian nurse leaders and organizations are similarly invested. PURPOSE: The purpose of this analysis was to explore the policies of Canadian nursing regulatory bodies and associations on nursing practice specific to environmental health. METHODS: A content analysis of nursing focused position statements and competency documents was conducted to assess Canadian nursing policies in environmental health. Publicly available position statements and competency documents regarding health and the environment were retrieved from Canadian nursing regulatory colleges and nursing associations, the Canadian Nursing Associations, and the International Council of Nurses. All documents were coded inductively and thematically analyzed. RESULTS: In total, 22 documents were retrieved which consisted of 11 policy statements from nursing associations and 11 competency documents from nursing regulators and national associations. Four themes were generated: collaboration, language of engagement, nursing actions, and social justice. CONCLUSION: There is a gap between nursing policies and competencies directing nursing action related to the health of the environment across Canada. There is an opportunity to improve eco-literacy within the nursing profession, undergraduate education and to produce nursing research on environmental health.
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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 | 0.022 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".