Picture perfect? Gazing into girls’ health, physical activity, and nutrition through photovoice
Bibliographic record
Abstract
Background: Women face contradictions regarding their health: Pressure to be feminine, but also athletic; Criticism for being too sporty or muscular, but equally so for being perceived as lazy or overweight. These complexities are perpetuated through media and discourse.Purpose: Using a feminist post-structural approach and photovoice, this study explored health, physical activity, and nutrition in adolescent girls and young women.Methods: Photovoice enables reflection, promotes dialogue, and sparks change. The process involved conducting a workshop, collecting photos, and participatory analysis sessions, which engaged the participants (n = 7, ages 13–26) in photo selection, contextualization, and codifying.Results: This resulted in three themes: First, (Breaking) Stereotypes, in which participants identified gender norms, conflicts, and contradictions; Second, Emotional Safety, or the contexts in which girls and young women feel confident and comfortable; Finally, Being Outside in Nature emerged as significant. Each theme is supported by quotations and photographs. This work suggests being outside in nature provides important context for girls and young women to feel emotionally safe, such that they may engage in the complex navigation of competing discourses surrounding health.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".