WORKING TOGETHER TO PROVIDE GERIATRIC CARE: INTERDISCIPLINARY TEAM CREATION AND EVALUATION IN CANADA
Bibliographic record
Abstract
Abstract Prince Edward Island is Canada’s smallest province (pop. 160,000) and has one of the oldest populations in Canada. While there is a robust and growing geriatrics program through provincial healthcare, it consists exclusively of medical providers (i.e., geriatricians and nurse practitioners). An opportunity for formal interprofessional collaboration presented itself when the University of Prince Edward Island began a Doctor of Psychology (PsyD) program in 2019. Soon thereafter, we established an interdisciplinary team between the PsyD and the provincial geriatrics programs. The goals of the team included: 1) enhancing care for older adults on the island, 2) providing training for PsyD students, and 3) increasing interprofessional collaboration and learning. In an evaluation of the first year of the collaboration, we collected data on the types of referrals and characteristics of the patients referred (e.g., number of medications), and asked all providers on the team (N=4 geriatricians, N=9 nurse practitioners, and N=4 psychology student clinicians) to answer open ended questions on the highlights and challenges of establishing the team, the organization of formal team meetings, and provision of patient care. Of the 74 patients seen by psychology, reasons included cognitive or psychodiagnostic assessment (N=22, 30%), psychotherapy (N=20, 27%), caregiver support (N= 25, 34%), or multiple services (i.e., both assessment and intervention; n=7, 10%). Additional patient characteristics will be shared, as well as themes from provider survey responses. Our program has implications for other areas in rural North America, particularly training opportunities built into our team functioning.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".