Barriers to patient care in southwestern Ontario rural emergency departments: physician perceptions.
Bibliographic record
Abstract
INTRODUCTION: We sought to determine the perceptions of physicians staffing rural emergency departments (EDs) in southwestern Ontario with respect to factors affecting patient care in the domains of physical resources, available support and education. METHODS: A confidential 30-item survey was distributed through ED chiefs to physicians working in rural EDs in southwestern Ontario. Using a 5-point Likert scale, physicians were asked to rate their perception of factors that affect patient care in their ED. Demographic and practice characteristics were collected to accurately represent the participating centres and physicians. RESULTS: Twenty-seven of the 164 surveys distributed were completed (16% response rate). Responses were received from 13 (81.3%) of the 16 surveyed EDs. Most of the respondents (78%) held CCFP (Certificant of the College of Family Physicians) credentials, with no additional emergency medicine training. Crowding from inpatient boarding, and inadequate physician staffing or coverage in EDs were identified as having a negative impact on patient care. Information sharing within the hospital, access to emergent laboratory studies and physician access to medications in the ED were identified as having the greatest positive impact on patient care. Respondents viewed all questions in the domain of education as either positive or neutral. CONCLUSION: Our survey results reveal that physicians practising emergency medicine in southwestern Ontario perceive crowding as the greatest barrier to providing patient care. Conversely, the survey identified that rural ED physicians perceive information sharing within the hospital, the availability of emergent laboratory studies and access to medications within the ED as having a strongly positive impact on patient care. Interestingly, our findings suggest that physicians in rural EDs view their access to education as adequate, as responses were either positive or neutral in regard to access to training and ability to maintain relevant skills.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".