Challenges and Lessons Learned From a Mobile Health, Web-Based Human Papillomavirus Intervention for Female Korean American College Students: Feasibility Experimental Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) and Web-based research methods are becoming more commonplace for researchers. However, there is a lack of mHealth and Web-based human papillomavirus (HPV) prevention experimental studies that discuss potential issues that may arise. OBJECTIVE: This study aimed to assess the feasibility of research procedures and discuss the challenges and lessons learned from an mHealth and Web-based HPV prevention experimental study targeting female Korean American college students in the United States. METHODS: A pilot randomized controlled trial (RCT) was conducted in an mHealth and Web-based platform with 104 female Korean American college students aged 18-26 years between September 2016 and December 2016. Participants were randomized to either the experimental group (a storytelling video intervention) or the comparison group (a nonnarrative, information-based intervention). Outcomes included the feasibility of research procedures (recruitment, eligibility, randomization, and retention). RESULTS: From September 2016 to October 2016, we recorded 225 entries in our initial eligibility survey. The eligibility rate was 54.2% (122/225). This study demonstrated a high recruitment rate (95.6%, 111/122) and retention rate (83.7%, 87/104) at the 2-month follow-up. CONCLUSIONS: Findings from this study demonstrated sufficient feasibility in terms of research procedures to justify a full-scale RCT. Given the increased possibility of invalid or misrepresentative entries in mHealth and Web-based studies, strategies for detection and prevention are critical. TRIAL REGISTRATION: ISRCTN Registry ISRCTN12175285; http://www.isrctn.com/ISRCTN12175285.
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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 | 0.203 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".