Visual and Personalized Quality of Life Assessment App for People With Severe Mental Health Problems: Qualitative Evaluation
Bibliographic record
Abstract
BACKGROUND: QoL-ME is a digital visual personalized quality of life assessment app for people with severe mental health problems. Research reveals that e-mental health apps frequently suffer from low engagement and fall short of expectations regarding their impact on patients' daily lives. Studies often indicate that e-mental health apps ought to respect the needs and preferences of end users to achieve optimal user engagement. OBJECTIVE: The aim of this study was to explore the experiences of users regarding the usability and functionality of QoL-ME and whether the app is actionable and beneficial for patients. METHODS: End users (n=8) of QoL-ME contributed to semistructured interviews. An interview guide was used to direct the interviews. All interviews were audiorecorded and transcribed verbatim. Transcriptions were analyzed and coded thematically. RESULTS: Analysis revealed 3 main themes: (1) benefit, (2) actionability, and (3) characteristics of the QoL-ME. The first theme reveals that the QoL-ME app was beneficial for the majority of respondents, primarily by prompting them to reflect on their quality of life. The current version is not yet actionable; the actionability of the QoL-ME app may be improved by enabling users to view their scores over time and by supplying practical advice for quality of life improvements. Overall, participants had positive experiences with the usability, design, and content of the app. CONCLUSIONS: The QoL-ME app can be beneficial to users as it provides them with insight into their quality of life and elicits reflection. Incorporating more functionalities that facilitate self-management, such as advice and strategies for improving areas that are lacking, will likely make the app actionable. Patients positively regarded the usability, design, and contents of the QoL-ME app.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".