An intelligent activity-based client-centred training system: a pilot study on motivation, usability and credibility in persons with central nervous system diseases
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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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".