High acuity rural transport: findings from a qualitative investigation
Bibliographic record
Abstract
INTRODUCTION: The High Acuity Response Team (HART) was introduced in British Columbia (BC), Canada, to fill a gap in transport for rural patients that was previously being met by nurses and physicians leaving their communities to escort patients in need of critical care. The HART team consists of a critical care registered nurse (CCRN) and registered respiratory therapist (RRT) and attends acute care patients in rural sites by either stabilizing them in their community or transporting them. HART services are deployed in partnership with provincial ambulance services, which provide vehicles and coordination of all requests in the province for patient transport. This article presents the qualitative findings from a research evaluation of the efficacy of the HART model, including staffing and inter-organizational functioning. METHOD: Open-ended qualitative research interviewing was done with key stakeholders from 21 sites. Research participants included HART CCRNs, RRTs, administrative leads, as well as local emergency department (ED) physicians and nurses. Thematic analysis was done of the transcripts. RESULTS: A total of 107 interviews in 21 study sites were completed. Participants described characteristics of the model, perceptions of efficacy and areas for improvement. Rural sites reported a decrease in physician- and nurse-accompanied transports for high-acuity patients due to the HART team, but also noted challenges in delayed deployment, sometimes leading to adverse patient outcomes. CONCLUSIONS: The salient issues for the HART model were grounded in a somewhat artificial distinction between pre-hospital and interfacility transport for rural patients, which leads to a lack of service coordination and potentially avoidable delays. A beneficial systems change would be to move towards dedicated integration of high-acuity transport services into hospital organizational structures and community health services in rural areas.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".