Usability, learnability and performance evaluation of Intelligent Research and Intervention Software: A delivery platform for eHealth interventions
Bibliographic record
Abstract
Evaluation of an eHealth platform, Intelligent Research and Intervention Software was undertaken via cross-sectional survey of staff users and application performance monitoring. The platform is used to deliver psychosocial interventions across a range of clinical contexts, project scopes, and delivery modalities (e.g. hybrid telehealth, fully online self-managed, randomized control trials, and clinical service delivery). Intelligent Research and Intervention Software supports persuasive technology elements (e.g. tailoring, reminders, and personalization) as well as staff management tools. Results from the System Usability Scale involving 30 Staff and Administrative users across multiple projects were positive with overall mean score of 70 ("Acceptable"). The mean score for "Usability" sub-scale was 82 and for "Learnability" sub-scale 61. There were no significant differences in perceptions of usability across user groups or levels of experience. Application performance management analytics (e.g. Application Performance Index scores) across two test sites indicate the software platform is robust and reliable when compared to industry standards. Intelligent Research and Intervention Software is successfully operating as a flexible platform for creating, delivering, and evaluating eHealth interventions.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".