Creation and pilot testing of StrokEngine: A stroke rehabilitation intervention website for clinicians and families
Bibliographic record
Abstract
OBJECTIVE: There is a gap in the translation of knowledge about stroke between researchers and clinicians. This paper describes the creation and pilot testing of an evidence-based stroke rehabilitation intervention website, StrokEngine (http://www.strokengine.org), which was designed to close this gap. DESIGN: A within-subject design was used to compare the usability and navigability of StrokEngine vs other search strategies/sites. Each participant searched a well-known stroke website, searched StrokEngine, and performed a free search, with the order of search randomized. A standard questionnaire was used to elicit information on usability and navigability across the 3 searches. SUBJECTS: A purposive sample of 19 rehabilitation clinicians from Montreal, Quebec, with varied stroke-related treatment experience. RESULTS: All 19 clinicians gave the highest usability score to StrokEngine (p<0.05): StrokEngine usability score (mean 43, SD 4) vs the Cochrane Library (mean 26, SD 8), the Royal College of Physicians website (mean 20, SD 5) and a general Internet search (mean 26, SD 7). CONCLUSION: This preliminary study on StrokEngine's usability and navigability suggests that it has the potential to be an asset for clinicians who wish to keep abreast of information on intervention effectiveness.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".