Scale strategies in the promotion of youth health: systematization of the experience through a technological platform in Central America and beyond
Bibliographic record
Abstract
Digital health platforms hold promise for addressing youth health inequalities; however, the factors that enable their successful expansion remain to be explored in depth. This study examines the scaling process of JovenSalud.net, a nonprofit digital platform for adolescent health promotion in Central America, led by TeenSmart International, as part of the 18-month 'Transition to Scale' initiative (2022-2023). The evaluation combined platform analysis (41,550 new registrations; 9087 sexual and reproductive health enrollments; 1739 course completions) and experience systematization as a methodology. Quantitative findings demonstrated substantial improvements in sexual and reproductive health knowledge and attitudes among graduates, along with increased condom use, rejection of unsafe sex practices, awareness of STIs and breast health, and understanding of the benefits of delaying sexual activity. From these data, five critical success factors emerged: (1) unique value proposition and competitive advantage, (2) technological innovation and modernization, (3) strategic alliances, (4) a diversified marketing and promotion strategy, and (5) a monitoring and evaluation (M&E) systems. Conversely, five key barriers were identified: (1) complex regulatory and political environments impede the formation of sustained advocacy partnerships, (2) limited promotional budgets constrain effective marketing and user outreach, (3) unstable, diversified funding streams challenge long-term financial sustainability, (4) technological inequities and low digital skills hinder platform adoption and (5) continuous technological change demands ongoing investment in team skills and infrastructure. Clearly defining and preserving core intervention components within adaptive M&E systems proved essential for maintaining fidelity and enabling real-time optimization, while sustained investment in organizational capacity and user-centered design underpins the long-term, scalable impact of nonprofit digital health initiatives for adolescents.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| 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".