ACEING THE CARE OF OLDER ADULTS IN HOSPITALS THROUGH INNOVATIVE MODELS OF ACUTE CARE FOR ELDERS
Bibliographic record
Abstract
At Mount Sinai Hospital, in Toronto Canada, the Acute Care for Elders (ACE) Strategy was conceived as a multi-component intervention incorporating a series of evidence-informed but tailored inter-professional interventions (i.e., ISAR Screening, GEM, ACE Units, HELP, House Calls etc.) to improve the care of hospitalized older adults. Starting from fiscal year 2010/11 onwards, a number of evidence informed interventions were gradually implemented each year in a variety of individual patient settings while also looking to address the important issue of transitions especially between hospital and home. The ACE Strategy links these interventions to create a more seamless, integrated inter-professional and team-based delivery-model spanning the continuum of care Our proposed symposium will explore outcomes related to the implementation of this innovative strategy through four talks: 1) “Establishing the Effectiveness of an Acute Care for Elders (ACE) Strategic Delivery Model” by Dr. Samir Sinha 2) “Measuring the Impact of the GEM Nursing Role in the Emergency Department Setting” by Ms. Nana Asomaning, 3) “Outcomes of a Quality Improvement Intervention to Reduce Unnecessary Urinary Catheter Utilization” by Dr. Richard Norman and 4) “Hospitalization and Place-of-Death Among Homebound Older Adults in a Home-Based Primary Care Program” by Dr. Nathan Stall. The goal of our symposium is to review this innovative strategy for supporting the acute care needs of the elderly. The symposium will conclude with an interactive discussion exploring the facilitators and barriers to the implementation of effective and integrated acute care models for the elderly.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".