Exploring the Use of Technology for Sexual Health Risk-Reduction among Ecuadorean Adolescents
Bibliographic record
Abstract
BACKGROUND: There is a lack of sexual health knowledge and resource access among youth in Latin America, along with rising rates of teenage pregnancy and STD transmission. OBJECTIVE: To determine baseline sexual health knowledge and the acceptance of a technology based sexual health risk-reduction program among Ecuadorean adolescents. METHODS: We used mixed methods to determine the sexual health knowledge and practices, and technology use among 204 adolescents from two schools in Cumbayá and Lumbisí, Ecuador. Quantitative data was collected through surveys and qualitative through single-gender focus groups. FINDINGS: Nearly every participant (96.6%) expressed interest in a sexual health education program using technology and social media. A majority of participants indicated that they consulted parents (58.3%) regarding sexual health questions. Only a few participants had access to physicians outside of appointments (3.9%), and most desired more sexual health information (87.3%). Although approximately one-quarter of participants were sexually active (27%), most lacked baseline knowledge regarding contraceptives and STDs. Facebook (91%) and WhatsApp (53%) were the most frequently used and requested social media for an educational program. Students indicated a strong desire to be involved in the design stages of a sexual health risk-reduction program, rather than use a pre-established program. CONCLUSIONS: There is strong interest in a technology based sexual health risk-reduction program through Facebook and WhatsApp, which could establish communication between health providers and Ecuadorian youth to disseminate health information and answer private inquiries. Findings from this study, the first of its kind among South American adolescents, introduces a novel idea: involving participants from initial design stages of a text-messaging health education program. Future studies should focus on engaging families as well as physicians' willingness to participate.Implications and Contributions: This paper is the first acceptability study of a technology based sexual health risk-reduction program among low-income South American adolescents. Findings enhance understanding of pregnancy and STD prevention interventions by demonstrating participants' desire for self-design and implementation, and highlight their importance through a lack of baseline adolescent sexual health knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".