Nurse case management to improve risk reduction outcomes in a stroke prevention clinic.
Bibliographic record
Abstract
Stroke prevention clinic health care professionals are mandated to provide early access to neurological consultation and treatment, diagnostic testing, and behavioural risk factor management for clients with transient ischemic attack or mild non-disabling stroke. Clinic nurses collaborate with clients and interprofessional teams to support risk factor reduction to prevent recurrent stroke events. Although hypertension is the most important modifiable risk factor for stroke, broader evidence indicates that adherence to prescribed medications may be less than 50%. One clinic identified a need to improve risk factor outcomes through identifying clients with uncontrolled hypertension, cognitive, self-eficacy and/or adherence characteristics predictive of non-achievement of blood pressure targets. To address this need, an expanded nurse case management care delivery model was pilot tested for feasibility in a participant sample of 20 clients. Motivational interviewing and self-management approaches were combined with interventions designed to improve adherence:facilitation of the simplification of medication routines, providing memory cues and home self-monitoring equipment, counselling, and six-month nursing follow-up. Results demonstrated that an expanded nurse case management model of care delivery is feasible with only a modest impact on clinic resources. At six months, there were significant reductions in blood pressure and increases in medication self-efficacy and adherence for selected clients identified with high risk for stroke and non-achievement of treatment outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".