Examining How Chronically Ill Patients’ Reactions to and Effective Use of Information Technology Can Influence How Well They Self-Manage Their Illness
Bibliographic record
Abstract
While chronically ill patients can significantly benefit from self-management (SM) information systems, they are also unlikely to perceive, use, and benefit from them in the same way, and past research has observed that many patients tend to not use such systems effectively. A key premise of the present study is that attributing the cause of one’s success or failure in self-managing one’s chronic disease to SM information systems is likely to influence how patients react to such systems, which in turn is likely to influence their usage behaviors and SM performance. Building upon attribution theory and learned helplessness theory, this paper examines how patients’ causal attributions of their success or failure in self-managing their chronic illness tends to influence the way they cognitively perceive, emotionally react to, and use an IT-based SM system. It also examines what constituted effective use in the SM context that was studied and how patients’ effective use of an IT-based SM system tended to influence their SM performance. Based on data collected from patients who were using a web-based asthma SM portal, the paper identifies three SM attributional styles and three patient views of SM information systems that help explain how chronically ill patients tend to interact with such systems, as well as the consequences of this interaction, and discusses the implications of the findings for research and practice.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".