Stroke rehabilitation information for clients and families: Assessing the quality of the<i>StrokEngine-Family</i>website
Bibliographic record
Abstract
PURPOSE: This study: (i) Identified the availability of scientifically-based information on the internet regarding stroke rehabilitation intended for those who have experienced a stroke and their families; and, (ii) assessed the usability of a newly created website on stroke rehabilitation for laypersons, StrokEngine-Family. METHOD: First, an extensive systematic search was undertaken to identify and appraise existing stroke rehabilitation websites. Seventeen websites met specific inclusion/exclusion criteria. Although some addressed stroke rehabilitation interventions in layperson language, none discussed the numerous treatment options based on scientifically based information. Thus, StrokEngine-Family was developed and its usability assessed with individuals who had experienced a stroke and family members. RESULTS: Seven respondents aged 43-68 years participated in the pilot testing of the newly developed StrokEngine-Family. All except one indicated overall satisfaction with the website: The one respondent rated it as somewhat user-friendly mainly for aesthetic reasons including the need for darker colors and larger font. In addition, respondents requested specific information regarding emotional support and local community referrals to this type of support. Based on the feedback, minor changes were made including a greater use of short phrases, bulleted notations and the addition of a depression module. CONCLUSIONS: The systematic review provided support for the development of StrokEngine-Family. In pilot testing, StrokEngine-Family was easy to use and valuable in content.
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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 | 0.035 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".