Characteristics of exercise- and nutrition-based continuing education for health-care professionals to support community-living older adults in Canada
Bibliographic record
Abstract
Health-care professionals (HCPs) have an important role in disseminating and implementing exercise and nutrition care to older adults to support aging in place. We performed an environmental scan to examine the availability of exercise- and nutrition-based continuing education (CE) resources for kinesiologists, physiotherapists, exercise physiologists, occupational therapists, dietitians, and pharmacists in Canada relating to the specific needs of community-living older adults. Data were collected between 2023 and 2024 using websites of provincial and national organizations for HCPs, advocacy organizations for chronic conditions, and aging institutes; communication with knowledge users; and a targeted Google search. Sixty-nine courses were identified that have been active within the past 5 years. Notably, only 9% focused exclusively on nutrition compared to 64% that focused exclusively on exercise. Also, most courses (74%) delivered content aimed at age-adjacent chronic health conditions, while few described their content with respect to healthy aging specifically. While courses emphasized evaluation and application in their course objectives, these goals were inconsistent with the fact that most courses had short durations (<1.5 h) and were delivered virtually and asynchronously. Our work suggests that the organizations we scanned provide limited education on nutrition for older Canadians. Furthermore, across both exercise and nutritional education, in-person or hybrid options to support the training and application of practical skills are lacking. Exercise and nutrition-focused CE development would support HCP learners to help their older clients age in place.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".