Emergency Departments as the Health Safety Nets of Society: A Descriptive and Multicenter Analysis of Social Worker Support in the Emergency Room
Bibliographic record
Abstract
Introduction Social Work (SW) referrals made in the emergency department (ED) highlight the weaknesses in the existing support system for vulnerable and disadvantaged patients. SW personnel play a pivotal role in some EDs but are not integrated into the team in several jurisdictions. Our objective was to provide a detailed description of the need for SW support in the ED setting by describing SW consultation patterns in an urban ED location. Methods A three-year analysis of ED SW referrals made through a network of four acute care hospitals serving a city population of 1.2 million inhabitants where social workers operate from 8 a.m. to 10 p.m. The study design was descriptive reporting proportions. The descriptors of interest were the types of ED patients receiving SW consultations and the reasons for patient referral to the SW Department. Results During the study period, there were 46,970 SW consultations, representing 8.02% of the 572,804 patients who visited the ED across Calgary, yielding 42.9 referrals per day to social workers through the ED. Consultations for domestic violence were three times more prevalent for women (6% of referrals). However, domestic violence consultations were still an active issue for men (1.9%). Comparisons by age group yielded illness adjustments (15.3%), discharge planning (31.2%), and legal decision making (23.9%) as the most common reasons for referral of patients over 75 years old; 92.8% of patients over 75 years were admitted following the SW consultation. Reasons for deferral of patients under 30 years of age were illness adjustments (12.2%), discharge planning (16.4 %), and legal decision making (1.4%); 57.3% of patients under 30 years were admitted following the consultation. Addiction/drug use and homelessness were more common in those under the age of 30, comprising 24.1% and 15.4% of the SW referrals, respectively, compared to 1.6% and 0.4% of referrals for those over age 75, respectively. Conclusions The demand for SW support is significant and complex in these large urban EDs. However, the impact on patient care and resource use is substantial, and the data indicates that SW integration may be of universal benefit to EDs. Further studies are warranted to accurately characterize the amount and type of SW necessary for optimal patient outcomes and hospital resource use.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".