Barriers to environmentally sustainable initiatives in oral health care clinical settings.
Bibliographic record
Abstract
Human health is linked to environmental health. Pollutants or disease-causing microbes released into the environment through human activity or natural disasters affect communities' air quality, water or food supply, and, ultimately, the livelihoods of residents. Oral health care (OHC) professionals, including dental hygienists (DHs), use vast amounts of resources in their daily clinical operations, which contribute to the global pollution burden and climate change. Canadian OHC professionals are largely missing from the environmental sustainability dialogue, despite their commitment to the holistic well-being of their clients and communities they support. Objective: This literature review explores the barriers to adopting environmentally sustainable (ES) initiatives in the clinical setting as perceived by OHC professionals, particularly DHs. Results: Eight studies reviewed highlight 4 key barriers-infrastructural, institutional, educational, and individual-to the adoption of ES initiatives by OHC professionals in the clinical setting. Conclusion: OHC professionals who adopt ES initiatives to curb the potential environmental impacts of their clinical practices support the population health of the communities they serve and, thus, the well-being of future generations. Further research may guide the development of education, protocol, policy, and infrastructure changes to facilitate the adoption of ES initiatives by OHC professionals even amidst ever-changing global conditions. Adopting ES initiatives not only benefits the environment, but it may also aide in improving client outcomes due to long-term practice savings that can be diverted to enhancing client care.
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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 | 0.029 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".