Usability, learnability and performance evaluation of Intelligent Research and Intervention Software: A delivery platform for eHealth interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.031 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it