Academy of Plant-based Physical Therapy: overdue to address a nutrition crisis with a transformative population approach
Bibliographic record
Abstract
This timely evidence synthesis supports the need for an Academy of Plant-based Physical Therapy. Given epidemiological and empirical evidence and the profession's values and practice scope, the time has come for a specialty of plant-based physical therapy based on population health principles. This review connects these factors. Non-communicable diseases (NCDs) are largely nutrition-related resulting from unnatural elements of our diet (i.e., heart disease, several cancers, hypertension, stroke, diabetes, obesity, gastrointestinal diseases, autoimmune diseases, renal disease, and Alzheimer's disease). Most adults, even children, have NCD risk factors or manifestations. Alternatively, plant-based nutrition can prevent, manage, as well as potentially reverse these diseases, as well as augment conventional physical therapy outcomes by reducing inflammation and pain. Proposed competencies for plant-based physical therapists include high-level competency in health and NCD risk assessments/evaluations, to establish population health-informed nutrition needs for maximal health, healing and repair, in turn, function and wellbeing; and assessment of patients' nutrition-related knowledge, beliefs/attitudes, self-efficacy, and readiness-to-change. Population-informed nutritional counseling is initiated as indicated. An Academy of Plant-based Physical Therapy could advance the profession globally at this point in history and also serve as a role model to other health professions through practicing evidence-based, plant-based nutrition built upon population health principles.
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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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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".