The Clinical Impact of eHealth on the Self-Management of Diabetes: A Double Adoption Perspective
Bibliographic record
Abstract
The development, adoption, and acceptance of eHealth systems that change and improve patient self-care have been promising, but the results have been mixed and the work mostly atheoretical. In this paper, we respond to this opportunity by developing and assessing an eHealth system for newly diagnosed type 2 diabetes patients. Study participants used the eHealth system for a 12-month period after diagnosis in an attempt to acquire an understanding about their diabetes, develop self-care activities (e.g., blood glucose testing), and improve their biomedical outcomes. Drawing upon theories and methods from information systems and upon the Precede-Proceed model of health promotion planning, we explored the double adoption of eHealth technology and its antecedents, self-care practices and their antecedents, and improvements in biomedical outcomes important to long-term diabetes health. Path model results indicate important implications for information systems, eHealth, and health promotion practice and research, which are discussed.
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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 | 0.012 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".