A Long-Term Care—Comprehensive Geriatric Assessment (LTC-CGA) Tool: Improving Care for Frail Older Adults?
Bibliographic record
Abstract
BACKGROUND: Most older adults living in long-term care facilities (LTCF) are frail and have complex care needs. Holistic understanding of residents' health status is key to providing good care. Comprehensive Geriatric Assessment (CGA) is a valid assessment method which aims to embrace complexity. Here we aimed to study a CGA that has been modified for use in long-term care (the LTC-CGA) and to investigate its acceptability and usefulness to stakeholders and users. METHODS: This mixed methods study, conducted in 10 LTCFs in Halifax, Nova Scotia, reviewed 598 resident charts from pre- and post-implementation of the LTC-CGA. Qualitative methods explored stakeholder perspectives (physicians, nurses, paramedics, administrators, residents and families) though focus groups. RESULTS: The LTC-CGA was present in 78% of LTCF charts in the post -implementation, period though it did not appear in acute care charts of transferred residents, despite the intention that it accompany residents between care sites. Some items had suboptimal completion rates (e.g., Advance Directives at 56.4%), though these were located in other sections of the LTCF chart (98.2%). Nevertheless, qualitative findings suggest the LTC-CGA describes a clinical baseline health status which enabled timely and informed clinical decision-making. CONCLUSIONS: The LTC-CGA is a useful resource whose full capacity may not yet have been realized.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".