Usability and Satisfaction Testing of Game-Based Learning Avatar-Navigated Mobile (GLAm), an App for Cervical Cancer Screening: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Barriers to cervical cancer screening in young adults include a lack of knowledge and negative perceptions of testing. Evidence shows that mobile technology reduces these barriers; thus, we developed a web app, Game-based Learning Avatar-navigated mobile (GLAm), to educate and motivate cervical cancer screening using the Fogg Behavioral Model as a theoretic guide. Users create avatars to navigate the app, answer short quizzes with education about cervical cancer and screening, watch videos of the screening process, and earn digital trophies. OBJECTIVE: We tested ease of use, usefulness, and satisfaction with the GLAm app among young adults. METHODS: This mixed methods study comprised a qualitative think-aloud play interview session and a quantitative survey study. Participants were cervical cancer screening-eligible US residents aged 21 to 29 years recruited through social media. Qualitative study participants explored the app in a think-aloud play session conducted through videoconference. Data were analyzed using directed content analysis to identify themes of ease of use, usefulness, and content satisfaction. Qualitative study participants and additional participants then used the app independently for 1 week and completed a web-based survey (the quantitative study). Ease of use, usefulness, and satisfaction were assessed using the validated Technology Acceptance Model and Computer System Usability Questionnaire adapted to use of an app. Mean (SD) scores (range 1-7) are presented. RESULTS: A total of 23 individuals participated in one or both study components. The mean age was 25.6 years. A majority were cisgender women (21/23, 91%) and White (18/23, 78%), and 83% (19/23) had at least some secondary education. Nine participants completed the think-aloud play session. Direct content analysis showed desire for content that is concise, eases anxiety around screenings, and uses game features (avatars and rewards). Twenty-three individuals completed the quantitative survey study. Mean scores showed the app was perceived to be easy to use (mean score 6.17, SD 0.27) and moderately useful to increase cervical cancer screening knowledge and uptake (mean score 4.94, SD 0.27). Participants were highly satisfied with the app (mean score 6.21, SD 1.20). CONCLUSIONS: Survey results showed participants were satisfied with the app format and found it easy to use. The app was perceived to be moderately useful to inform and motivate cervical cancer screening; notably, the screening reminder function was not tested in this study. Qualitative study results demonstrated the app's ability to ease anxiety about screening through demonstration of the screening process, and brevity of app components was favored. Interpretation of results is limited by the predominantly cisgender, White, and educated study population; additional testing in populations which historically have lower cervical cancer screening uptake is needed. A modified version of the app is undergoing efficacy testing in a randomized clinical trial.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".