GAUGING INTEREST AND NEEDS IN PROFESSIONAL DEVELOPMENT AND CONTINUING EDUCATION IN AGING IN MANITOBA, CANADA
Bibliographic record
Abstract
Abstract The Centre on Aging at the University of Manitoba in Canada conducted a needs assessment on continuing education (CE) and professional development (PD) in aging with people who work and connect with older adults. During winter and spring 2022, an online survey of 35 questions asked respondents about their own or their staff and volunteer topics of interest, as well as logistics of CE/PD workshop training. A total of 146 participants responded. Respondents were from different regions of Manitoba, with the highest proportion (42%) located in the Winnipeg region. About 86% were female and 12% male, ranging in age from 18 to 75: with most falling in the 35 to 64 age range. Respondents were asked to indicate their level of interest for topics on a five-point scale from Not Interested to Very Interested. The topics Aging Through the Lifespan and Wellness received the highest number of responses, while Indigenous Aging received the least. Across the 1,712 individual ratings of suggested topics, the highest proportion were rated as interested (35%) and very interested (31%); while 19% rated level of interest as moderate, 11% minimal, and 4% not interested. When asked about potential workshop formats, the majority (63%) preferred a mix of both in-person and online delivery formats with preference for 1–2-hour workshops and a certificate as proof of completion. About two-thirds indicated a requirement to complete CE/PD credits to maintain related credentials. These findings provide considerations for higher education institutions on CE/PD for those working and connecting with older adults.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".