Methodology of a Cross-sectional Study Evaluating the Impact of a Novel Mobile Care Team on the Prevalence of Ambulatory Care Sensitive Conditions Presenting to Emergency Medical Services
Bibliographic record
Abstract
Introduction Hospitalization due to ambulatory care sensitive conditions (ACSC) is often used as a proxy measure for access to primary care. The prevalence of ACSC has not been measured in the prehospital setting. Emergency medical services (EMS) are being used by patients who lack access to primary care for ACSC. Many novel models of care have been implemented within Canada and internationally, utilizing paramedics to ease the burden of poor primary care access. Recently, a mobile care team (MCT) consisting of a paramedic/nurse configuration has been deployed in the community of New Waterford, Nova Scotia. The team responds to low acuity 911 calls and follow-up appointments booked by primary care clinicians. This study will identify the prevalence of patients with ACSC presenting to EMS before and after the implementation of MCT and the differences after the implementation of the MCT. Methods Secondary data will be collected from the centralized EMS electronic patient care report (ePCR) database. All patients presenting to the ground ambulance with ACSC during the year prior to MCT implementation, all patients presenting to the ground ambulance with ACSC during the year post-MCT implementation, and all patients presenting to the MCT with ACSC will be included for analysis, allowing for a calculation of ACSC prevalence. Descriptive methods will be used for age, sex, primary care practitioner, and ASCS complaints. Prevalence data will be compared via the chi-squared test. A subgroup analysis of age, sex, and individual presenting conditions will also be analyzed using the chi-squared test. Confounding will be dealt with via multivariate logistic regression. Results The study results are pending; however, a literature review reveals a paucity of data on ACSC in EMS. Conclusions Due to the paucity of literature surrounding ACSC prevalence in EMS, the methodology developed to study these prevalence rates is a novel protocol of importance to prehospital research and the epidemiology of ACSC more broadly.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.001 |
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".