MyVoice National Text Message Survey of Youth Aged 14 to 24 Years: Study Protocol
Bibliographic record
Abstract
BACKGROUND: There has been little progress in adolescent health outcomes in recent decades. Researchers and youth-serving organizations struggle to accurately elicit youth voice and translate youth perspectives into health care policy. OBJECTIVE: Our aim is to describe the protocol of the MyVoice Project, a longitudinal mixed methods study designed to engage youth, particularly those not typically included in research. Text messaging surveys are collected, analyzed, and disseminated in real time to leverage youth perspectives to impact policy. METHODS: Youth aged 14 to 24 years are recruited to receive weekly text message surveys on a variety of policy and health topics. The research team, including academic researchers, methodologists, and youth, develop questions through an iterative writing and piloting process. Question topics are elicited from community organizations, researchers, and policy makers to inform salient policies. A youth-centered interactive platform has been developed that automatically sends confidential weekly surveys and incentives to participants. Parental consent is not required because the survey is of minimal risk to participants. Recruitment occurs online (eg, Facebook, Instagram, university health research website) and in person at community events. Weekly surveys collect both quantitative and qualitative data. Quantitative data are analyzed using descriptive statistics. Qualitative data are quickly analyzed using natural language processing and traditional qualitative methods. Mixed methods integration and analysis supports a more in-depth understanding of the research questions. RESULTS: We are currently recruiting and enrolling participants through in-person and online strategies. Question development, weekly data collection, data analysis, and dissemination are in progress. CONCLUSIONS: MyVoice quickly ascertains the thoughts and opinions of youth in real time using a widespread, readily available technology-text messaging. Results are disseminated to researchers, policy makers, and youth-serving organizations through a variety of methods. Policy makers and organizations also share their priority areas with the research team to develop additional question sets to inform important policy decisions. Youth-serving organizations can use results to make decisions to promote youth well-being.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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".