Greening Family Medicine clinic operations and clinical care, <i>where do we start?</i> A scoping review of toolkits and aids
Bibliographic record
Abstract
BACKGROUND: There is a pressing need for healthcare to respond to the climate crisis. Family physicians, given their central role in community healthcare provision, are strategically placed to lead, support, and promote sustainable healthcare, yet guidance on how to do this is fragmented. OBJECTIVE: To identify and evaluate toolkits and aids on sustainable healthcare to act as a curated resource for family physicians and their care teams interested in delivering evidence-based sustainable healthcare in their clinical practices. METHODS: A scoping review was completed of the published and grey literature across 4 databases and 2 search engines to identify articles and aids/toolkits from 1990 to present. Toolkits were subsequently evaluated for purpose, evidence-base, implementation process, adaptability to family medicine, and outcome measures. RESULTS: The search identified 17,751 articles. Screening resulted in 20 published articles and 11 toolkits. Most articles presented simple checklists to support greening clinic initiatives, 3 studies focussed on partial carbon footprint analyses, and 4 on educational initiatives. Toolkits ranged in sustainability topics and degree of depth covered, and adaptability and outcome measures. None of the resources identified have been formally evaluated for effectiveness. CONCLUSIONS: A range of aids exist to support greening of clinic operations; however, there is a significant gap in the literature for greening clinical care. Two toolkits were found to be comprehensive, one requiring tracking and reporting of sustainability initiatives. This scoping review provides a starting point for motivated family doctors and community clinics to initiate change and support more sustainable healthcare.
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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 | 0.084 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".