Using the Healthy Native Youth Implementation Toolbox to Provide Web-Based Adolescent Health Promotion Decision Support to American Indian and Alaska Native Communities: Implementation Study
Bibliographic record
Abstract
Background: American Indian and Alaska Native (AI/AN) youth experience numerous health inequities, including those in sexual, reproductive, and mental health. Implementation of culturally relevant, age-appropriate evidence-based programs may mitigate these inequities. However, numerous barriers limit the adoption and implementation of evidence-based adolescent health promotion programs in AI/AN communities. Objective: This study examines user reach and engagement from 2022 to 2024 of web-based decision support (the Healthy Native Youth [HNY] website and the embedded HNY Implementation Toolbox), designed to increase the implementation of evidence-based adolescent health promotion programming in AI/AN communities. Methods: Promotional strategies were designed for optimal geographic reach to Tribal organizations, opinion leaders, federal decision makers, and funders. Promotional channels included grassroots, community, and professional networks. We used Google Analytics to examine the uptake of the HNY website and HNY Implementation Toolbox from January 2022 to January 2024. The Toolbox provides culturally relevant tools and templates to help users navigate through 5 phases of program adoption and implementation: Gather, Choose, Prepare, Implement, and Grow. User reach was estimated by demographic characteristics and geographic location; user engagement was estimated by visit frequency and duration, bounce rates, and frequency of page and tool access. Results: Over the study period, page views of the HNY website and HNY Toolbox increased 10-fold and 27-fold, respectively. Over the 2-year evaluation period since the Toolbox "go live" date, approximately 1 in 8 users of the HNY website visited the Toolbox. The majority of HNY website users were located in Washington (n=1515), California (n=1290), and Oregon (n=1019) and were aged between 18 and 24 (n=1559, 21.7%) and 25-34 (n=1676, 23.29%) years. Toolbox users were primarily located in California (n=1238), Washington (n=1142), and Oregon (n=986), mostly aged between 35 and 44 years (n=444, 35%). Both website and Toolbox users were primarily female, who accessed the site and Toolbox via desktop computers. The most frequently accessed phase pages within the Implementation Toolbox were Gather, Choose, Implement, and Prepare, as supported by bounce rates and average time on page. The most viewed phase was the "Gather" phase, with 3278 views. The most frequently downloaded tools within the Toolbox were Gather: Community Needs and Resource Assessment, with 136 downloads. The phases and tools accessed may have differed based on the user's goal or stage of implementation. Conclusions: Findings indicate positive initial reach and engagement of the HNY website and HNY Implementation Toolbox among AI/AN educators that has consistently increased over the 2 years. The provision of web-based decision support that guides AI/AN users through the adoption, implementation, and maintenance of culturally relevant, age-appropriate, evidence-based adolescent health promotion programs in their communities may help increase the implementation of effective adolescent health promotion programs to ultimately increase health equity among AI/AN youth.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".