Creation and preliminary validation of the screening for self-medication safety post-stroke scale (S-5)
Bibliographic record
Abstract
RATIONALE AND OBJECTIVE: Patients with stroke should be screened for safety prior to starting a self-medication regime. An extensive literature review revealed no standardized self-medication tool tailored to the multi-faceted needs of the stroke population. The aim of this study was to create and validate a condition-specific tool to be used in screening for self-medication safety in individuals with stroke. DESIGN: Items were generated using expert consultation and review of the existing tools. The draft tool was pilot-tested on expert stroke clinicians to receive feedback on content, clarity, optimal cueing and domain omissions. The final version was piloted on patients with stroke using a structured interviewer-administered interview. RESULTS: The tool was progressively refined and validated according to feedback from the 11 expert reviewers. The subsequent version was piloted on patients with stroke. The final version includes 16 questions designed to elicit information on 5 domains: cognition, communication, motor, visual-perception and, judgement/executive function/self-efficacy. CONCLUSION: The Screening for Safe Self-medication post-Stroke Scale (S-5) has been created and validated for use by health professionals to screen self-medication safety readiness of patients after stroke. Its use should also help to guide clinicians' recommendations and interventions aimed at enhancing self-medication post-stroke.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".