An intelligent activity-based client-centred training system: a pilot study on motivation, usability and credibility in persons with central nervous system diseases
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Bibliographic record
Abstract
Introduction: Clinicians and rehabilitation centres are searching for affordable technology-supported systems that incorporate a client-centred task-oriented approach which increase client’s motivation and adherence without extra costs and extra individual therapy time. In order to meet these requirements, the intelligent Activity-based Client-centred Taskoriented Training (i-ACT) was developed via user-centred design. Objective: To evaluate the motivation, usability, credibility and treatment expectancy of i-ACT and treatment effect on upper limb functional ability. Method: In four rehabilitation centres, a mixed method longitudinal study was performed. Training with i-ACT was provided for 6 weeks, 3x/week, 45 min/day, additional to treatment as usual. Data collection was performed at baseline, after 2 weeks, 4 weeks and 6 weeks of training and 8-10 weeks after training completion. Semi-structured interviews were conducted with therapists and clients after 6 weeks of training. Results: Seventeen persons with central nervous system diseases participated. Motivation scores on the Intrinsic Motivation Inventory remained high on all subscales (≥ 5.2/7.0), except pressure (≤ 2.0/7.0). Similarly, high scores were seen throughout on the System Usability Scale (≥ 73.8/100) and Credibility/Expectancy Questionnaire (≥ 22.0/27.0, ≥ 15.8/27.0 respectively). Results on upper limb functioning showed a significant progress over time (p<.05). Significant improvement over time was also found on self-perception with the Canadian Occupational Performance Measure (p<.05). Results from the interviews corroborate the findings of the quantitative results. Furthermore, therapists and clients also considered i-ACT user-friendly and affordable. Conclusion: i-ACT is a client-centred task-oriented system with great potential in neurorehabilitation to increase motivation and assist improvement on functional level.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it