Development of a Mobile Health Intervention to Promote Papanicolaou Tests and Human Papillomavirus Vaccination in an Underserved Immigrant Population: A Culturally Targeted and Individually Tailored Text Messaging Approach
Bibliographic record
Abstract
BACKGROUND: Disparities in cervical cancer incidence and mortality signify the need for intervention efforts targeting Korean American immigrant women. OBJECTIVE: The purpose of this study was to demonstrate how a culturally targeted and tailored mobile text messaging intervention, mobile screening (mScreening), was developed to promote the uptake of Papanicolaou tests and human papillomavirus vaccine among young Korean American immigrant women. METHODS: Guided by the Fogg behavior model, the mScreening intervention was developed through a series of focus groups. Braun and Clarke's thematic analysis was used to identify core themes. RESULTS: Overall, 4 themes were identified: (1) tailored message content (ie, basic knowledge about cervical cancer), (2) an interactive and visual message format (ie, age-appropriate and friendly messages using emoticons), (3) brief message delivery formats to promote participant engagement, and (4) use of an incentive to motivate participation (ie, gift cards). CONCLUSIONS: This study demonstrated the processes of gathering culturally relevant information to develop a mobile phone text messaging intervention and incorporating the target population's perspectives into the development of the intervention. The findings of the study could help guide future intervention development targeting different types of cancer screening in other underserved racial or ethnic groups.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".