The SCOPE Intervention: Impact of a Social Care Optimization Pilot Initiative in the Emergency Department
Bibliographic record
Abstract
Emergency departments (EDs) across the globe are in a state of crisis. It is becoming increasingly difficult for hospitals to manage ED flow given the rising number of patients and subsequently, the inability of hospitals to meet such demands (Jarvis, 2016). Issues of overcrowding, long wait times, and unnecessary admissions in the ED are reported across many countries resulting in negative outcomes for patients (that is, increased rates of morbidity and mortality) and for hospitals (that is, financial loss) (Bywaters, McLeod, Fisher, Cooke, & Swann, 2011; Cassarino et al., 2019; Chang, Abujaber, Reynolds, Camargo, & Obermeyer, 2016). EDs in Canada and the United States are no exception. Li and colleagues (2007) reported that U.S and Canadian ED utilization rates are similar, such that the annual rate of ED visits is approximately 40 visits per 100 members of the population. It is not surprising that the high rates of ED visits and hospital admissions coincide with the increasing numbers of patients presenting with complex care challenges that encompass not only clinical care, but social care as well (for example, homeless people, domestic violence victims, and patients with special needs) (Cassarino et al., 2019). As such, the health care system requires better practice approaches that effectively address the increasing psychosocial needs of patients presenting 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".