A retrospective study on emergency visits in two hospitals in Sherbrooke, Canada
Bibliographic record
Abstract
Non-urgent use of Emergency Departments throughout Canada has long presented a conundrum for hospital admini- strators and health service planners. On the one hand, perceptions persist that those non-urgent users contribute to overcrowding, higher costs of care and longer wait times. On the other hand, non-urgent users do not appear to increase wait times for high-acuity patients; they perceive their condition to be acute, or claim not having convenient access to primary medical services. The objective of this study is to investigate factors associated with emergency demand for minor conditions using administrative data as well as geographical and socioeconomic characteristics as captured by Pampalon’s deprivation indexes. We reviewed 42 months of administrative data (2006 – 2009) of minor emergency visits in two hospitals in Sherbrooke, QC, Canada. Data mining algorithms were applied to classify the visits and detect major utilization patterns of Sherbrooke residents. Lower priority visits (CTAS 5) continued to increase in the city hospital following a remodel. Adult residents tend to choose the closest ED, and children mainly go to the regional hospital ED. The use of ED for minor conditions (CTAS level 4 and 5) was higher in the most deprived communities, whether materially or socially. The most common diagnostic codes were injuries and poisoning, ill-defined conditions, respiratory problems, digestive system problems, musculoskeletal, and mental health conditions, with 15% of the visits not completed because patients left before seeing a doctor. Two Sherbrooke boroughs, with no walk-in clinics, generated the majority (57%) of all non-urgent visits. We forecast the reduction in the number of visits to ED if two walk-in clinics were opened in these boroughs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".