Implementing a new patient navigator model of care within the emergency department for older adults in Ontario, Canada
Bibliographic record
Abstract
A Patient Navigator (PN) role was introduced in the Emergency Department (ED) in a large metropolitan hospital in Southern Ontario (Canada) to assist with care transitions. The purpose of this study was to describe the new PN program and type of services provided for older adults in the ED. Given the novelty of the program, it is critical to better understand how a PN ED model of care may help improve the discharge process and ED-community transitions for older adults. This retrospective observational cohort study includes data between November 2020 and October 2021. In this study, the clinical data collected by the PN were analyzed to describe the patient socio-demographics, types of services provided, and outcomes. The PN contacted 95% patients (n = 125) referred to the service in which the median age was 80 (SD = 9.0) consisting of mostly females (74%; n = 92). The PN provided consultations to 79 patients (≤7 days) and 46 patients were admitted to the PN's caseload. For the 46 admitted cases, the PN connected to 52% of the patients on the same day, facilitated 83% of the patients in returning home or supportive setting and provided follow-up care (i.e., phone calls or home visits) for 67 days (median) in the community. This study provides a preliminary depiction of the scope of practice of a PN within an ED setting, and important considerations for decision-makers and/or administrators interested in implementing a PN role in the ED.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".