The Geriatrician-Led Comprehensive Geriatric Assessment Across Healthcare Settings: an Economic and Qualitative Evaluation
Bibliographic record
Abstract
In Canada, the number of older adults aged ≥65 years is expected to grow from 7.0 million in 2021 to 10.7 million in 2040. Older adults often have complex medical needs that are best managed using a comprehensive geriatric assessment (CGA). Although the CGA can be conducted by various health providers, geriatricians are specially trained to provide this intervention across different healthcare settings, such as acute care, community clinics, rehabilitation and long-term care. However, the number of geriatricians in Canada are limited, with 0.57 geriatricians per 10,000 older adults in 2019. With a limited workforce, it is necessary to identify ways to optimize the use of geriatricians. Preliminary findings from a network meta-analysis suggested that a geriatrician-led CGA has different effectiveness in different settings. The primary objective of this thesis was to determine the cost-effectiveness of a geriatrician-led CGA in different healthcare settings (study 2). Other objectives included determining: (i) the effectiveness and pooled event rates in the geriatric rehabilitation setting (study 1), the latter of which was used in the cost-effectiveness study; and (ii) the acceptability and feasibility (including barriers and facilitators) to implementing the geriatrician-led CGA in the cost-effective settings (study 3). In study 1, a systematic review and meta-analysis demonstrated that geriatric rehabilitation in the inpatient and day hospital settings was effective in reducing mortality, reducing long-term care home admission, and improving functional status. In study 2, a cost-effectiveness microsimulation model determined that the combination of acute care and rehabilitation was undominated for conducting a geriatrician-led CGA, with an option to add the community clinic setting if resources permitted. In study 3, a qualitative study using one-on-one interviews with patients, care partners, physicians and health administrators demonstrated that the geriatrician-led CGA was acceptable and valuable in all settings, with divergent views on whether the acute care or community clinic setting was most important for geriatricians to staff. Barriers to implementing a CGA were generally due to resource limitations. Overall, this thesis offers a strategy to preferentially staff geriatricians in certain healthcare settings in the context of a national geriatrician shortage to make our system efficient and sustainable.
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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 | 0.105 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".