Understanding Task-Performance Chain Feed-Forward and Feedback Relationships in E-health
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
The associations between the use of effective technology and user performance, and the effect of user performance on technology use and task-technology fit (TTF), requires further research (Furneauz, 2012). To address this call for future research, we examined the feed-forward from use and TTF to performance and the feedback from performance to use and TTF by using longitudinal data (n = 156) collected from participants using two custom-built e-health systems that we designed to provide education to develop self-management practices for study participants with newly diagnosed type 2 diabetes. We captured participants’ use of the two systems, their perceptions of TTF, and their health performance through biomedical outcomes every three months over a 12month period. Our findings show significant and different feed-forward and feedback relationships. In general, our results also show that system use and a negative TTF-use interaction significantly affected performance through feed-forward, while participant performance significantly affected use and negatively affects TTF through feedback. We discuss the implications for task-performance chain (TPC) research and developing and using e-health systems in chronic care.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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