A population-based exploration of immigrants undergoing general surgery procedures in British Columbia: Do immigrants present for emergency surgeries more than non-immigrants?
Bibliographic record
Abstract
BACKGROUND: Canada's growing immigrant population faces language and cultural barriers that hinder timely access to healthcare. The balance between elective and emergency general surgery (EGS) reflects immigrant's access to healthcare since many EGS cases are avoidable through treatment as elective procedures. OBJECTIVE: This study examines whether immigrants are more likely to undergo EGS than non-immigrants and measures whether language proficiency or access to primary care plays a role in disparity in access to care. METHODS: All general surgery procedures performed in British Columbia, Canada between 2013 and 2021 were identified using a population-based longitudinal administrative data that linked immigration data with physician billing and hospital data. The primary outcome was whether patients' surgery was elective or EGS and the primary exposure was immigrant status. The odds of EGS between immigrants and non-immigrants was estimated adjusting for patient and system-level differences. The analysis compared immigrants with and without English proficiency on arrival to Canada. RESULTS: Of 237,054 general surgery procedures, 30.7 % were EGS and 15.2 % involved immigrants. Immigrants had slightly higher odds of undergoing emergency general surgery (EGS) than non-immigrants. Immigrants not fluent in English had 16 % higher odds of EGS (OR: 1.16, 95 %CI 1.03-1.32). Immigrants with fewer GP contacts were more likely to undergo EGS (45.5 % versus 42.2 %, p < 0.01). CONCLUSIONS: Immigrants with language barriers and who accessed primary care less often were more likely to require EGS. These findings highlight the need for system-level interventions to reduce immigrants' reliance on emergency surgical care.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".