Project Gender NutriScope: Methods of a Mixed Methods, Community-Engaged Study Design to Explore the Nutritional Needs of Transgender and Gender-Diverse Youth and Young Adults
Bibliographic record
Abstract
OBJECTIVE: Despite growing evidence of distinct nutrition-related experiences and disparities, transgender and gender-diverse (TGD) youth and young adults are an underrepresented population in nutrition research. This paper describes the methods and study design from Project Gender NutriScope, a study that will explore the nutritional needs of TGD youth and young adults. DESIGN: Parallel convergent cross-sectional mixed methods; community-engaged. SETTING: A purposive sample will be recruited in a Midwest city through clinics, youth organizations, and a large state university. PARTICIPANTS: Transgender and gender-diverse youth and young adults aged 13-24 years. INTERVENTION: Findings from this formative study will be used to inform future intervention development. MAIN OUTCOME MEASURES: Dietary intake, eating patterns, disordered eating patterns, food security status, perceptions of relationship with food, and nutrition-related concerns. ANALYSIS: Quantitative data will be analyzed using descriptive summary statistics. Qualitative data will be analyzed by reflexive thematic analysis. The 2 databases will be integrated iteratively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".