Environmental competencies for healthcare educators and trainees: A scoping review
Bibliographic record
Abstract
Background: The health-care community has a responsibility to address the environmental impact of delivering health-care services. Educational programmes present ideal fora to confer ‘environmental competencies’ to future health system leaders, managers, practitioners and researchers. The aim of this review is to synthesise the literature on health-care competencies, education and training of relevance to issues of environmental sustainability and climate change in the health sector. Methods: We conducted a systematic review of English language articles on environmental competencies in healthcare in the MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and ProQuest databases published from inception to April 2019. Thematic analysis and descriptive statistics were used to synthesise the findings and develop a typology of environmental competencies. Results: Of the 902 unique citations, we identified 23 articles that met our inclusion criteria. Environmental competencies that align with general management skills were most frequent (40%), followed by research skills (37%). Three competencies specific to the environment (22%) were identified: resource stewardship ( n = 16), systems thinking ( n = 14) and social and environmental justice ( n = 7). The majority of work was identified in nursing, medicine and public health. Competencies were most commonly embedded in existing curricula or offered as new courses or workshops. Conclusion: Incorporating environmental competencies into education programmes will serve to raise awareness of, and advance the significance of, the health sector’s impact on climate change and environmental sustainability. Our findings that environmental competencies align with previously validated health-care competencies support the perspective that these competencies represent essential knowledge and skills for the health-care workforce.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".