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Record W2770036491 · doi:10.1055/s-0037-1608647

An Electronic Referral Initiative to Facilitate Referral to a Chronic Disease Self-Management Program for Persons with Transient Ischemic Attack

2017· article· en· W2770036491 on OpenAlexaff
Dorothy Kessler, Amir Afkham, Aline Bourgoin, Sophia Gocan, Brammiya Sivakumar, Mary Windsor, Clare Liddy

Bibliographic record

VenueACI Open · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsOttawa HospitalUniversity of OttawaBruyèreBaycrest Hospital
Fundersnot available
KeywordsReferralMedicineStroke (engine)General partnershipDisease managementFamily medicinePhysical therapyMedical emergencyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.080
GPT teacher head0.385
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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