Raising the bar of care for older people in Ontario emergency departments
Bibliographic record
Abstract
AIM: To describe the role of geriatric emergency management nurses as a catalyst for culture change in emergency department processes with the goal to improve care and outcomes of older people. BACKGROUND: The changing context and literature has called for a culture change within emergency department care to integrate principles of older people care into care delivery. There is a paucity of reports describing how geriatric emergency care models bring about a broader change in culture within the entire emergency department. METHODS: The Ontario Ministry of Health and Long-term Care in Canada established a programme to place geriatric emergency management nurses into emergency departments with the goal to improve delivery of care through development of unique, site-appropriate solutions. RESULTS: Geriatric emergency management nurses incorporate capacity building into their role to develop and strengthen the skills, instincts, abilities, process and resources of the emergency department. Care processes focus on areas of staffing, mobilization, comfort, medication, hygiene, nutrition/hydration, cognition, environment, equipment and stimulation. Multi-modal educational strategies and advocacy promote appropriate person-centred care. Improved communication among care providers at key patient transition points remains a priority system-level improvement. CONCLUSION: Geriatric emergency management nurses work collaboratively with the emergency department team to facilitate change in the way that emergency department care is provided to the older person experiencing health emergencies. IMPLICATIONS FOR PRACTICE: Known strategies that have been effective in improving outcomes for older people within the hospital and residential care setting can be generalized into emergency department care. Further research into the effectiveness of these strategies in this environment is recommended.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".