Continuing Education for Gerontological Social Work: Findings From Post-Graduate Advanced Practice Training
Bibliographic record
Abstract
Abstract The social work profession aims to help all individuals, families, and communities enhance their overall well-being. While gerontological social workers primarily work with seniors, they are often tasked with addressing the needs of not only their senior client, but the client’s network as well. Social workers are also asked to stay current with respect to new legislation, policy, and systemic changes to help their clients. Thus, gerontological social workers often need to obtain advanced practice education in a number of areas, as related to gerontology. Responding to this gap, the National Initiative for the Care of the Elderly (NICE) and Sinai Health offered a series of innovative advanced practice gerontological social work courses - hybrid online and in-class - to those looking to improve their knowledge and competencies in the following five identified 'high impact' areas: (1) Medical Assistance in Dying (MAiD); (2) cultural competence and LGBTQI2S; (3) mental health; (4) legal issues and aging; (5) dementia. Participants in the courses completed pre- and post-surveys assessing knowledge, attitudes and competencies with the subject matter, with responses helping to improve understanding of how to provide the most appropriate resources for those who care for older adults and how to better shape future education/training programs. Findings suggest that gerontological social workers may benefit from 'tailored refresher courses' that bridge knowledge and practice gaps, that the optimal update time may be every three years, and that clinicians benefit from being trained by interdisciplinary teams, rather than by someone of the same profession.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".