Physician attendance during interhospital patient transfer in Ontario: 2005–2015
Bibliographic record
Abstract
INTRODUCTION: Interhospital transfer of patients may be attended by a variety of healthcare providers, including physicians. The role of physicians in ambulance transfer in Ontario is not well studied. This study aims to describe the cohort of physicians providing intra-ambulance patient care in Ontario from 2005 to 2015. Secondary outcomes of interest were geographical characteristics of physician-attended transfers and patient characteristics. METHODS: OHIP billing data were used to find all instances of physician-attended air or land ambulance transfer from 2005 to 2015. These data were matched to physician data from the Corporate Providers Database and the Institute for Clinical Evaluative Sciences Physicians Database to describe the physicians providing intra-ambulance care. Patient and geographical data came from the National Ambulatory Care Reporting System and Registered Persons Database to describe the rurality of physician-attended transfers and patient characteristics. RESULTS: There were 916-1216 physician-attended transfers performed by 508-639 unique physicians in any given year. Physicians were mostly family physicians without anaesthesia or emergency medicine training (58%), with CCFP-EM physicians accounting for 17% and family medicine anaesthetists 10%. Thirty-eight per cent of physicians providing intra-ambulance care practised in rural settings. Seventy-three per cent of physician-attended land transfers originated in suburban, rural or remote hospitals. CONCLUSIONS: Physician-attended ambulance transfer in Ontario is largely provided by family physicians in suburban to remote settings. This may have implications for the education of resident physicians in this unique skill set. Further research is needed into current education practices in intra-ambulance care.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".