Professional Development Preferences and Needs of Healthcare Providers Working with Older Adults on Exercise and Nutrition: Results of Key Informant Interviews
Bibliographic record
Abstract
Most older adults want to age in place, and evidence-based health behaviours that support aging in place include healthy diets and physical activity. Healthcare professionals need training in the science and practice of nutrition and physical activity to support their older adult clients to age in place. In this study we investigated knowledge gaps among healthcare professionals and the organizations that employ them regarding exercise and nutrition for older adults. We also aimed to identify their perceptions of effective and ineffective continuing education approaches, how they choose continuing education opportunities, and what formats they prefer when engaging in continuing education. Using key informant interviews and an interpretive description approach, we identified four themes: “Being pragmatic about professional development,” “Matching format to need,” “Negotiating the tension between the convenience of online and the effectiveness of in-person learning,” and “Focusing on practice is critical.” Participants also identified current gaps in professional development offerings and desires for additional continuing education opportunities on certain topics, such as nutrition and aging, and dealing with multimorbidity. Participants indicated that continuing education offerings should reflect common health conditions that providers encounter in practice and that there should be a balance between online and in-person offerings. Participants also indicated that continuing education should focus on changing or improving practice, to assist healthcare providers in supporting older adults aging in place in their communities.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".