END USER ENGAGEMENT IN DEVELOPING A SELF-CARE ONLINE APP
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Approximately half a million people in Canada suffer from heart failure (HF), a leading cause of hospital admission. HF outcomes can be improved by self-care behaviors, to which patients often show low adherence. This study focuses on the co-design of an online self-care application and community intervention, called HFApp, which patients with HF and their informal caregivers could use to potentially improve HF outcomes. The intended users for HFApp are older adults with HF and their informal caregivers. The primary objective of this study is to identify themes for the development of HFApp. The secondary objective is to apply these findings to identify user needs and preferences for HFApp. Persona-scenario discussion sessions were conducted with 4 older patients with HF (≥ 60 years) and 4 informal caregivers from the Hamilton Health Sciences Heart Function Clinic. One persona-scenario discussion session was held for each participant type (i.e. patients with HF or informal caregivers). Participants were divided into pairs and participant pairs created personas and scenarios together. Scenarios included: (1) how they learn about HFApp, (2) how they might access HFApp, (3) where they are when they use HFApp, (4) who might help them with HFApp, and (5) how often they use HFApp. All discussions were audio recorded. Data analysis, using NVivo 10 , provided six categories of design themes which were used to develop a list of user requirements for HFApp. Some of these requirements help users perceive HFApp to be more useful and give a sense of self-care confidence. However, some requirements may be excluded due to low feasibility. It is recommended that a larger persona-scenario group session be conducted in the future to support the requirements gathered in this study as well as identify any new requirements.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it