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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".