RF12 Outpatient surgical referrals from primary care providers for people experiencing homelessness: a chart review from Hamilton, Canada
Bibliographic record
Abstract
Introduction People experiencing homelessness (PEH) suffer from a high burden of surgical conditions and face many barriers to accessing care. Our aim was to describe the unmeasured and unmet need of surgical care through outpatient surgical referrals.Methods This is a retrospective review of electronic patient charts from the Shelter Health Network, a health care and social service organization serving PEH in Hamilton, Canada. The review spanned a two-year period from 2017-2018 and included referrals to all outpatient surgical services (except ophthalmology) and endoscopy.Results 167 surgical referrals were sent for 129 patients over the two-year period. The average age of patients was 46 years, and 95% had provincial health insurance. 95% of referrals resulted in a scheduled appointment, and 58% resulted in the patient seeing a surgical provider. Overall, 63 surgical procedures were proposed and 62% of these were completed. This completion rate was similar between minor and major procedures. Patient, provider and system factors contributed to patients not receiving care.Conclusions The vast majority of referrals resulted in a scheduled appointment, however half of PEH were seen by a surgical provider and just over half received a proposed surgical procedure. Barriers identified were divided into systemic barriers, provider barriers and patient barriers. This project was funded by the MacGlObAs Global Surgical Scholar Research Bursary, McMaster University, Canada.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".