Health Workers’ Perspectives on Mobile Health Care Learning Stickiness: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Doctor-to-Doctor (D2D) is a mobile learning app that aims to support continuous learning in health care, commonly known as continuing medical education. One of the metrics of success in mobile learning is the average amount of time spent each month on the app, which is a component of stickiness, the tendency of users to use apps repeatedly. Stickiness metrics are important because stickiness has a direct effect on user retention. OBJECTIVE: This study aimed to determine the factors influencing user stickiness of the D2D mobile learning app. The research framework was based on the stimulus-organism-response theory. METHODS: This study used a mixed methods approach, including a web-based questionnaire (quantitative data) and interviews (qualitative data). We recruited 520 health worker respondents, including general practitioners, dentists, specialists, and medical students, as users of the D2D app. Quantitative data processing was conducted using covariance-based structural equation modeling, whereas qualitative analysis was conducted on the data from 15 respondents using the content analysis method. RESULTS: On the basis of the web-based questionnaire (quantitative) results, we found that cognitive (P=.01) and emotional (P=.004) app relationship quality affected health workers' stickiness in mobile learning. On the other hand, factors related to the functionality of the app and health workers' experience were proven to affect cognitive and emotional app relationship quality (P<.005). In addition, according to interview (qualitative) data, the performance of apps for mobile learning is influenced by information quality and information processing speed, which are needed to deliver a more efficient learning process and reduce the possibility of misunderstanding in the interpretation of learning materials. The user experience is influenced by gamification factors to make the learning process more fun, especially for medical students who do not have to obtain professional credit units (referred to as satuan kredit profesional in Indonesia), unlike physicians or specialists. CONCLUSIONS: The results of this study will help mobile learning service providers increase user stickiness in mobile learning, for example, through processing speed, the quality of the information presented, security features, personalized content recommendations, and gamification.
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".