Leading the Way in Naturopathic Gerontology: Current Status and Future Possibilities
Bibliographic record
Abstract
his educational void started with the lack of training in and exposure to seniors' health topics at the naturopathic medical school level, and it is our understanding that this still has not changed. For leaders in naturopathic gerontology to emerge, it requires exposure to this field within the core curriculum of the naturopathic educational program. In reviewing the curriculum of the Canadian naturopathic colleges there was a lack of content related to geriatrics, gerontology or seniors' health. The Canadian College of Naturopathic Medicine (CCNM) does not include geriatrics/ gerontology as part of the core curriculum. 1 When questioned about this deficit, we were informed that although geriatric topics are currently infused in many of the courses, there is no course that focuses on geriatrics specifically. 2 However, the core and/or elective curriculum does include such special topics as: pregnancy, labour and newborn care; pediatrics; emergency medicine; mental health; and sexual and reproductive health.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".