An Electronic Referral Initiative to Facilitate Referral to a Chronic Disease Self-Management Program for Persons with Transient Ischemic Attack
Bibliographic record
Abstract
Background Transient ischemic attack (TIA) is a strong predictor of subsequent stroke. Measures to decrease stroke incidence following TIA include medical management, risk factor optimization, and lifestyle modification. Best practice recommendations for stroke care promote individualized education on self-management across transitions from hospital to community settings. In the study region, patients with TIA are referred to the stroke prevention clinic where they receive rapid stroke assessment, risk factor management, education, and referral to risk reduction programs. Long-term management is typically the responsibility of primary care providers who may have limited resources for self-management support. Promotion of existing chronic disease self-management (CDSM) programs can complement this support. Objective The objective of our project was to examine the feasibility and acceptability of an electronic referral system to an existing CDSM program to facilitate self-management support for persons with TIA. Methods We performed a descriptive evaluation of a quality improvement project that involved development and implementation of a new electronic referral (eReferral) process. A partnership between the stroke prevention clinic and the regional Living Healthy CDSM program was developed alongside a clinical information system redesign implementing an eReferral system. Results Referral to the Living Healthy CDSM program was offered to each patient at the stroke prevention clinic. Of 912 patients seen over a 6-month period, 62 (7%) agreed to be referred. Of these, 23 (37%) were registered or waitlisted. Conclusion Formation of a partnership and implementation of the eReferral system facilitated referral to the Living Healthy CDSM program. Despite low referral and enrollment rates, the eReferral system provides one option to enhance self-management support for persons with TIA.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".