User-centered design of a web-based self-management site for individuals with type 2 diabetes – providing a sense of control and community
Bibliographic record
Abstract
BACKGROUND: To design and test a web-based self-management tool for patients with type 2 diabetes for its usability and feasibility. METHODS: An evidence-based, theory-driven website was created for patients with type 2 diabetes. Twenty-three patients with type 2 diabetes aged ≥ 25 years were recruited from 2 diabetes care centers in Toronto, Canada. We employed focus group methodology to assess acceptability, sustainability, strengths and weaknesses of the self-management website. Based on these results, revisions were made to the website. Three cycles of individual usability testing sessions using cognitive task analysis were conducted with patients with type 2 diabetes. Revisions to the website were made based on results from this testing. RESULTS: We identified five themes concerning participants' experiences of health care and related unmet needs: 1) Desire for information and for greater access to timely and personalized care to gain a sense of control of their disease; 2) Desire for community (sharing experiences with others) to fulfill practical and emotional needs; 3) Potential roles of an online self-management website in self-empowerment, behavior change, self-management and health care delivery; 4) Importance of a patient-centered perspective in presenting content (e.g. common assumptions, medical nomenclature, language, messaging, sociocultural context); 5) Barriers and facilitators to use of a self-management website (including perceived relevance of content, incorporation into usual routine, availability for goal-directed use, usability issues). CONCLUSIONS: Participants outlined a series of unmet health care needs, and stated that they wanted timely access to tailored knowledge about their condition, mechanisms to control and track their disease, and opportunities to share experiences with other patients. These findings have implications for patients with type 2 diabetes of diverse ages, socioeconomic backgrounds, and disease severity, as well as to the design of other computer-based resources for chronic disease management.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".