Effective Use of Patient-Centric Health Information Systems: the Influence of Patient Emotions
Bibliographic record
Abstract
The present study examined how patients’ emotional responses to a Portal (i.e., a pa- tient-centric health IT designed to help patients self-manage their chronic condition) influ- enced their effective use of the Portal. Based on interview data collected from 34 asthma patients, we identified six categories of emotions that the Portal’s usage evoked in patients who participated in the study. While patients who had negative emotions about the Portal tended to always use it ineffectively, the effectiveness with which patients who had positive emotions used the Portal varied according to their differing perceptions of the Portal. In addition, while all positive emotions were associated with high frequencies of Portal use, this usage was not always effective as it was sometimes not aligned with the Portal’s goal of asthma self-management. These findings suggest that designers and implementers need to pay greater attention to the emotional responses that patient-users can have, and to try to minimize the emergence of negative emotions by designing systems that induce in patients a positive experience and self-image, as well as joy while promoting their effective usage of these systems.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".