Patient and paramedic experiences with a direct electronic referral programme for focused hypoglycaemia education following paramedic service assist‐requiring hypoglycaemia in London and Middlesex County, Ontario, Canada
Bibliographic record
Abstract
AIMS: Hypoglycaemia is a common treatment consequence in diabetes mellitus. Prior studies have shown that a large proportion of people with paramedic assist-requiring hypoglycaemia prefer not to be transported to hospital. Thus, these episodes are "invisible" to their usual diabetes care providers. A direct electronic referral programme where paramedics sent referrals focused hypoglycaemia education at the time of paramedic assessment was implemented in our region for 18 months; however, referral programme uptake was low. In this study, we examined patient and paramedic experiences with a direct electronic referral programme for hypoglycaemia education postparamedic assist-requiring hypoglycaemia, including barriers to programme referral and education attendance. METHODS: We surveyed paramedics and conducted semistructured telephone interviews of patients with paramedic-assisted hypoglycaemia who consented to the referral programme and were scheduled for an education session in London and Middlesex County, Canada. RESULTS: Paramedics and patient participants felt that the direct referral programme was beneficial. A third of paramedics who responded to our survey used the referral programme for each encounter where they treated patients for hypoglycaemia. Patients felt very positive about the referral programme and their paramedic encounter; however, they described embarrassment, guilt and prior negative experience as key barriers to attending education. CONCLUSIONS: Paramedics and patients felt that direct referral for focused hypoglycaemia education postparamedic assist-requiring hypoglycaemia was an excellent strategy. Despite this, referral programme participation was low and thus there remain ongoing barriers to implementation and attendance. Future iterations should consider how best to meet patient needs through innovative delivery methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".