Using Comprehensive Geriatric Assessment in Identifying Care Goals and Referral Services in a Frailty Intervention Clinic
Bibliographic record
Abstract
The proportion of older adults and frail adults in Canada is expected to rise significantly in upcoming years. Currently, a considerable number of older adults do not actively participate in developing their own care plans; prior research has indicated several benefits of patient engagement in this process. Thus, we conducted a mixed methods study that examined the prevalence of rehabilitation goals and identified these for 305 community dwelling older adults referred to a frailty intervention clinic utilizing Comprehensive Geriatric Assessment (CGA) between 2014 and 2018. Top patient concerns included mobility (84%), services, systems, and policies (51%), sensory functions and pain (50%), and self-care or domestic life (47%). The most common referrals or recommendations for patients included further follow-up with a physician or specialist (36%), referral to an onsite falls prevention clinic (31%), and medication modifications (31%). Based upon these findings, we recommend greater utilization of CGA within a team-based approach to improve patient care by allowing for greater collaboration and shared decision-making by health-care providers. Moreover, CGA can be an effective tool to meet the complex and unique health-care needs of frail patients while incorporating patient goals. This is vitally important considering the predicted growth in the population of frail and/or older patients, as well as the current challenges and shortfalls in meeting the health-care needs of this population.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".