Geriatric emergency management nursing in Ontario
Bibliographic record
Abstract
Do you feel as though the majority of patients you see in the emergency department (ED) are older adults?For many ED health care providers, the impact of the aging demographic in Canada is perceived as immense.While older adults do use ED health services at proportionately higher rates, the average is only about 15% to 25% of ED visits in Ontario.Generally, older adults use ED services appropriately for emergency health situations.The coincident comorbidities and psychosocial issues can make older adult ED patients particularly complex and challenging.In the rapid and dynamic ED environment, these challenges can be overwhelming as ED nurses struggle to meet patients' needs.Because of these challenges, the Ontario Ministry of Health and Long-Term Care began funding Geriatric Emergency Management (GEM) nursing roles in 2004.The GEM nursing role focused on multi-dimensional aspects to service enhancement based on the Canadian Nurses Association advanced practice nursing framework (Canadian Nurses Association, 2002).Since the inception of the GEM role in 2004, there are now more than 30 GEM nurses within the province.Prior to GEM nursing in Ontario, there were pioneer researchers in GEM such as Dr. Jane McCusker from Montreal and Dr. Lorraine Mion from Cleveland.Their work helped to build a foundation for enhancing care of older adults in the ED setting and using the ED visit as an opportunity to assess and plan with older adults.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".