Improving care for residents in long term care facilities experiencing an acute change in health status
Bibliographic record
Abstract
Abstract Background Long term care (LTC) facilities provide health services and assist residents with daily care. At times residents may require transfer to emergency departments (ED), depending on the severity of their change in health status, their goals of care, and the ability of the facility to care for medically unstable residents. However, many transfers from LTC to ED are unnecessary, and expose residents to discontinuity in care and iatrogenic harms. This knowledge translation project aims to implement a standardized LTC-ED care and referral pathway for LTC facilities seeking transfer to ED, which optimizes the use of resources both within the LTC facility and surrounding community. Methods/design We will use a quasi-experimental randomized stepped-wedge design in the implementation and evaluation of the pathway within the Calgary zone of Alberta Health Services (AHS), Canada. Specifically, the intervention will be implemented in 38 LTC facilities. The intervention will involve a standardized LTC-ED care and referral pathway, along with targeted INTERACT® tools. The implementation strategies will be adapted to the local context of each facility and to address potential implementation barriers identified through a staff completed barriers assessment tool. The evaluation will use a mixed-methods approach. The primary outcome will be any change in the rate of transfers to ED from LTC facilities adjusted by resident-days. Secondary outcomes will include a post-implementation qualitative assessment of the pathway. Comparative cost-analysis will be undertaken from the perspective of publicly funded health care. Discussion This study will integrate current resources in the LTC-ED pathway in a manner that will better coordinate and optimize the care for LTC residents experiencing an acute change in health status.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".