Understanding Task-Performance Chain Feed-Forward and Feedback Relationships in E-health
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.
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.
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 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".