Education in stroke prevention: efficacy of an educational counselling intervention to increase knowledge in stroke survivors.
Bibliographic record
Abstract
BACKGROUND: Motivational interviewing and stages of change are approaches to increasing knowledge and effecting behavioural change. This study examined the application of this approach on stroke knowledge acquisition and changing individual lifestyle risk factors in an outpatient clinic. METHODS: RCT in which 200 participants were allocated to an education-counselling interview (ECI) or a control group. ECI group participants mapped their individual risk factors on a stage of change model and received an appointment to the next group lifestyle class. Participants completed a stroke knowledge questionnaire at baseline (T1), post-appointment, and three months (T3) post-appointment. Passive to active changes in lifestyle behaviour were self-reported at three months. RESULTS: There was a statistically significant difference between groups from T1 toT3 in stroke knowledge (p < 0.001). While there was a significant shift from a passive to active stage of change for the overall study sample (p < 0.000), there was no significant difference between groups on the identified risk factors. CONCLUSIONS: Although contact with patients in ambulatory clinical settings is limited due to time constraints, it is still possible to improve knowledge and initiate lifestyle changes utilizing motivational interviewing and a stage of change model. Stroke nurses may wish to consider these techniques in their practice setting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".