African American Emerging Adult Perspectives on Unintended Pregnancy and Meeting Their Needs With Mobile Technology: Mixed Methods Qualitative Study
Bibliographic record
Abstract
BACKGROUND: In the United States, a disproportionate number of unintended pregnancies occur among African Americans, particularly those in their later teenage years and early 20s. Mobile technology is becoming more ubiquitous as a method for health promotion; however, relatively little research has been done with this population to determine their perspectives about unintended pregnancy, the potential of successfully using mobile technology to prevent unintended pregnancy, and the content of such programs. OBJECTIVE: The purpose of this study was to obtain the perspectives of African American emerging adults about unintended pregnancy and the use of mobile technology to reduce unintended pregnancy rates. METHODS: Focus groups and interviews were conducted with 83 African Americans, aged 18-21 years. Data were analyzed using an open coding process. Emergent codes were then added as needed, and themes and subthemes were identified. RESULTS: Participants cited the social environment and lack of education as primary reasons for disproportionate rates of unintended pregnancy. They noted that unintended pregnancy is an important issue and that they desire more sexual health information. They enthusiastically supported mobile technology as a means to communicate unintended pregnancy prevention programming and offered many suggestions for program content, look, and feel. CONCLUSIONS: Young and emerging adult African Americans want and need sexual health resources, and a mobile-based platform could be widely accepted and address needs to lower disproportionate rates of unintended pregnancy. An essential next step is to use these findings to inform the development of a mobile-based unintended pregnancy prevention and sexual health program prototype to determine feasibility.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".