Navigating the Risks and Challenges of Advocacy to Improve Adolescent and Young Adult Health
Bibliographic record
Abstract
Even before the recent challenges to evidence-informed public health and clinical practice in the United States, advocating for systems changes to improve outcomes for young people has long carried risks, especially in resource-limited settings. The commentary by Sievings et al. [1] in this edition of the Journal of Adolescent Health offers valuable insights into the role of health professionals in using education as an advocacy tool to address the unique health needs of young people. It offers a framework and strategies for adolescent and young adult (AYA) professionals to improve health outcomes through evidence-informed practice, policy, and programs.
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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 | 0.056 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.046 | 0.081 |
| 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".