Planetary Health in Rehabilitation
Bibliographic record
Abstract
ABSTRACT: Sometimes out of necessity and sometimes out of convenience, medicine is wasteful. Resource stewardship is a critical and expanding field for reducing wasteful practices. Numerous international organizations are driving resource stewardship globally, including >20 countries worldwide participating in Choosing Wisely initiatives. However, opportunities for environmental stewardship have been long overlooked. Planetary health, climate action, and environmental stewardship in medicine consider opportunities which offer a co-benefit to the planet while improving or maintaining appropriate patient care across the healthcare continuum, including acute care, transitions in care, and postacute care, as well as in primary, secondary, and tertiary care settings. As rehabilitation is necessary across all healthcare settings, developing a culture among rehabilitation care providers that is conscientious about planetary health is imperative for sustainability of rehabilitation medicine and the health of our planet. We devised a recommendation for Choosing Wisely Canada's planetary health focus: don't dispose of adaptive equipment, mobility devices, orthoses, and prostheses that could be reused or recycled. This brief report discusses 1) why rehabilitation providers should engage with planetary health and climate action; 2) the rationale for the Choosing Wisely Canada Physical Medicine & Rehabilitation planetary health recommendation; and 3) existing avenues and novel opportunities for rehabilitation care providers worldwide to reduce waste in rehabilitation.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".