Designing and evaluating a web-based self-management site for patients with type 2 diabetes - systematic website development and study protocol
Bibliographic record
Abstract
BACKGROUND: Given that patients provide the majority of their own diabetes care, patient self-management training has increasingly become recognized as an important strategy with which to improve quality of care. However, participation in self management programs is low. In addition, the efficacy of current behavioural interventions wanes over time, reducing the impact of self-management interventions on patient health. Web-based interventions have the potential to bridge the gaps in diabetes care and self-management. METHODS: Our objective is to improve self-efficacy, quality of life, self-care, blood pressure, cholesterol and glycemic control and promote exercise in people with type 2 diabetes through the rigorous development and use of a web-based patient self-management intervention. This study consists of five phases: (1) intervention development; (2) feasibility testing; (3) usability testing; (4) intervention refinement; and (5) intervention evaluation using mixed methods. We will employ evidence-based strategies and tools, using a theoretical framework of self-efficacy, then elicit user feedback through focus groups and individual user testing sessions. Using iterative redesign the intervention will be refined. Once finalized, the impact of the website on patient self-efficacy, quality of life, self-care, HbA1c, LDL-cholesterol, blood pressure and weight will be assessed through a non-randomized observational cohort study using repeated measures modeling and individual interviews. DISCUSSION: Increasing use of the World Wide Web by consumers for health information and ongoing revolutions in social media are strong indicators that users are primed to welcome a new era of technology in health care. However, their full potential is hindered by limited knowledge regarding their effectiveness, poor usability, and high attrition rates. Our development and research agenda aims to address these limitations by improving usability, identifying characteristics associated with website use and attrition, and developing strategies to sustain patient use in order to maximize clinical outcomes.
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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 | 0.075 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".