Acceptability Testing of a Mobile Application Prototype for Tailored Patient Education and Self-Management Along the Transplant Journey
Bibliographic record
Abstract
The organ transplant journey is challenging and complex for patients. Tailored educational approaches, mobile health solutions, and patient-orientated research may positively impact health and wellbeing for those along the transplant journey. This study explores the prospective acceptability of the prototyped Health Education and Learning Platform (HELP), a mobile app for transplant education and self-management developed via user-centered design. A cross-sectional electronic survey based on the Theoretical Framework of Acceptability (TFA) was distributed via purposive and snowball sampling to transplant patients and care partners. Participants watched a 7-minute video illustrating the prototype prior to completing the survey. Likert-scale questions garnered prospective acceptability within the seven TFA component constructs of affective attitude, burden, ethicality, intervention coherence, opportunity costs, perceived effectiveness, and self-efficacy. Open-ended questions enabled participants to provide qualitative feedback. Data was collected using REDCap and analyzed using descriptive statistics and simple content analysis to categorize free-text responses to TFA component constructs. One hundred seventy-eight responses were received, of which 169 contained demographic information. All provinces in Canada were represented by at least one participant. Approximately half (51.6%) were patients. The majority were aged 31-50 (62.7%) and had completed some level of post-secondary education (76.2%). Overall, more than 70% of participants agreed or strongly agreed with acceptability items related to each of the TFA constructs. The highest affirmed TFA constructs were intervention coherence (83.3%) and self-efficacy (80.3%). Disparity between patients (83.9%) and care partners (53.1%) was observed within the affective attitude construct. Participants expressed overall positive regard for the prototype, including its design and novelty. There is positive interest across Canada in the HELP app as an acceptable tool for delivering transplant education and facilitating self-management. Participants endorse understanding how the app is designed to help, believe it would improve their ability to manage their health, and have confidence they could use it. Confirmation of prospective acceptability suffices to progress the prototype to beta testing.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".