FC8: Revising Competencies in Geriatrics for Canadian Medical Students: Adding a Mental Health Perspective
Bibliographic record
Abstract
In Canada, adults 85 years and older represent one of the fastest growing segments of the population1. Mood disorders and chronic illness often intersect, worsening health outcomes in late life2. In view of demographic trends, medical schools should ensure trainees are equipped with the knowledge, skills and attitudes to work with older adults. However, there continues to be much variation in how medical schools incorporate geriatric content into their curricula.In 2009, the Canadian Geriatrics Society (CGS) outlined 20 competencies in geriatrics to inform medical school curricula, but uptake was minimal. Of note, there were significant gaps in these competencies, which omitted mention of late-life depression. Geriatric mental health experts did not provide input.The objective of this project was to address gaps in geriatric competencies for medical students through an expert review process involving a biopsychosocial approach.Methods:The CGS established a 15-member national working group with representation from geriatric psychiatry, family medicine, a 95-year-old senior, geriatrics and medical trainees. Potential competencies were derived from existing Canadian geriatrics frameworks [Geriatrics 5M, CanMEDs] and 2009 competencies. A modified Delphi process yielded rankings for each competency using a 7-point Likert scale.Results:Between 2019 and 2021, 3 successive national surveys were completed. In the first (n=66), 34 competencies were identified. Agreement in the final survey was 87-95% (mean 90%). 51 participants completed all three. Significant topic omissions in the 2009 list of competencies were frailty, end-of-life care, delirium prevention, health promotion and the assessment and management of depression.Conclusions:Three national surveys expanded the core competencies in geriatrics for medical school curricula from 20 to 31. Expert consensus was high. Themes mapped along existing geriatrics frameworks and incorporated a holistic lens incorporating the perspectives of an older adult and geriatric psychiatrist. In addition to late-life depression, the importance of addressing ageism was also highlighted.Learning objectives for each competency are modifiable for level of training and individual program, offering flexibility. The CGS will continue to advocate for inclusion of updated, expanded competencies into training and licensure in geriatrics.1) Statistics Canada 2021, Canadian Government, accessed 1 January 2023, <. Accessed 20/01/23 <https://www12.statcan.gc.ca/census-recensement/2021/as-sa/98-200-X/2021004/98-200-x2021004-eng.cfm>1) Hall CA, Reynolds III CF. Late-life depression in the primary care setting: challenges, collaborative care and prevention. Maturitas. 2014 Oct; 79(2):147-52.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".