Survey and Interview Findings of an Environmental Scan of Perioperative Geriatric Models of Care in Canada*
Bibliographic record
Abstract
Background: Best practice recommendations support the implementation of perioperative geriatric care models that tailor to the specific needs of older adults undergoing surgery. The objective of this study was to describe the current proactive perioperative geriatric programs and pathways in Canadian hospitals. Methods: A survey of geriatricians, surgeons, and anesthesiologists practicing in Canada combined with phone interviews of a subset of participants were used to determine characteristics of perioperative geriatric pathways or programs including eligibility, team composition, and intervention elements. Results: Analysis of 132 survey respondents and 24 interviews showed 47% (40 out of 85) of hospitals described had elements of a perioperative geriatrics program and 20% had two or more elements. Eleven themes emerged including: how perioperative geriatric care programs built geriatric competencies in other health-care providers; geriatric assessment identified risks not captured in standard perioperative risk assessment; perceived value for patients and the health-care team; delirium prevention was addressed; most programs were reactive; most programs were informal; virtual care may be used to meet demand; successful implementation required system buy-in with collaboration across subspecialties; mechanisms to drive improvement were accountability and data evaluation; few clinicians with geriatric expertise; and other priorities limited program implementation. Conclusions: There were few hospitals in Canada with perioperative geriatric care models and even fewer with elements spanning the entire perioperative pathway. Strengths, weaknesses, opportunities, and threats to inform the implementation and sustainability of perioperative geriatric care in the Canadian context were identified in this national environmental scan.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".