Educators' Perspectives on Integrating Technology Into Sexual Health Education: Implementation Study
Bibliographic record
Abstract
BACKGROUND: In the last decade, the use of technology-based sexual health education has increased. Multiple studies have shown the feasibility of technology-based interventions, while a subset has also shown efficacy in improving youths' sexual health outcomes such as increased condom use and knowledge. However, little is known about health educators' experiences in integrating technology to augment sexual health curricula. OBJECTIVE: The purpose of this study was to assess the perceptions and experiences of health educators regarding the incorporation of technology into a sexual health education program designed for underserved youth in Fresno County, California, and to identify facilitators and challenges to incorporating technology into the in-person curriculum. METHODS: This implementation study used data collected as part of a cluster randomized controlled trial to evaluate In the Know (ITK), an in-person sexual health education curriculum that includes technology-based content, such as a resource locator, videos, and games, which can be accessed through a mobile app or website. Data from implementation logs from each cohort (n=51) and annual interviews (n=8) with health educators were analyzed to assess the health educators' experiences using the technology and adaptations made during the implementation. RESULTS: The health educators reported that technological issues affected implementation to some degree: 87% of the time in the first year, which decreased to 47% in the third year as health educators' familiarity with the app increased and functionality improved. Technology issues were also more common in non-school settings. Successes and challenges in 3 domains emerged: managing technology, usability of the ITK app, and youth engagement. The health educators generally had positive comments about the app and youth engagement with the technology-based content and activities; however, they also noted certain barriers to adolescents' use of the mobile app including limited data storage and battery life on mobile phones. CONCLUSIONS: Health educators require training and support to optimize technology as a resource for engaging with youth and providing sensitive information. Although technology is often presented as a solution to reach underserved populations, educational programs should consider the technological needs and limitations of the participants, educators, and settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/18060.
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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 | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".