16-LB: Patient and Paramedic Experiences with a Direct Electronic Referral Program for Hypoglycemia (HG) Education following Paramedic Service Assist–Requiring HG: A Qualitative Study
Bibliographic record
Abstract
Hypoglycemia requiring paramedic assistance negatively impacts outcomes in people with diabetes. Prior studies have shown only a small proportion of those with paramedic assist-requiring HG are brought to hospital. These episodes are thus “invisible” to the health care system. An innovative direct electronic referral program in which paramedics sent a referral for focused HG education to the Diabetes Education Centre (DEC) at the time of paramedic assessment was implemented for 18 months. Program uptake was lower than expected (133 referrals, 79 scheduled, and 50 attended). This qualitative study examines barriers and facilitators of DEC attendance for HG education after paramedic assist-requiring HG. We conducted semi-structured interviews of patients with paramedic-assisted HG and surveyed paramedics about their experiences. Of 34 paramedics, 30 (88%) attended at least 1 HG call in the study period and 26 (76%) used the referral program. Fourteen patients (18% response rate) participated: 8 men (57%); 13 (93%) age ≥ 50 yrs. Eight patients (57%) recalled the paramedic referral, 6 (43%) did not, and 4 (29%) thought they were referred by their family doctor. One patient thought attendance was mandatory to keep their drivers’ license. Themes identified from patient responses included: positive impact of diabetes education especially if delivered early after diagnosis, importance of spousal support and in-person education. Barriers to attendance were prior DEC attendance especially in those with long diabetes duration and embarrassment (failure to self-manage, memory loss around the HG event). While patients felt that focused HG education is an excellent strategy to reduce recurrent episodes, many had already attended similar sessions and felt further education was not needed. Hence, an important gap in providing HG education to patients with severe HG may be both system-based and disease-related. Disclosure S.L. Liu: Consultant; Self; Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Novo Nordisk Inc., Sanofi. A. Rosa: None. S.L. Sibbald: None. J. Mahon: None. D.R. Carter: None. M. Peddle: None. T. Spaic: Research Support; Self; Novo Nordisk Inc. Speaker’s Bureau; Self; Dexcom, Inc., Sanofi. Funding Academic Medical Association of Southwestern Ontario (INN16-004)
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".