Using Photovoice with At-risk Youth: In a Community-based Cooking Program
Bibliographic record
Abstract
PURPOSE: We examined the facilitators of and barriers to participants' application of cooking skills beyond Cook It Up!, a pilot community-based cooking program targeting at-risk youth aged 13 to 18. METHODS: Photovoice is a qualitative research method using still-picture cameras to document participants' health and community realities. Four participants photographed items they perceived as facilitators of or barriers to the application of cooking skills. At a facilitated discussion group, youth discussed why they took certain pictures and how the photos best exemplified facilitators and barriers. Participants agreed upon the themes arising from the dialogue. Data trustworthiness tools were used to ensure that themes arising from the dialogue truly represented participants' perspectives. RESULTS: Four major themes emerged as facilitators: aptitude, food literacy, local and fresh ingredients, and connectedness. Access to unhealthy foods was the only barrier that participants identified. Participants and researchers decided to advocate for the sustainability of community-based cooking programs offered for high school credit. Participants' photos would enhance advocacy efforts with education stakeholders. CONCLUSIONS: Cook It Up! provided youth with cooking techniques for healthy, economical, homemade meals, but proof was needed of the transferability of skills outside the program environment. Youth in this study identified important facilitators that enabled the continued use of their cooking skills, and one barrier. Findings underscore the importance of community-based cooking programs tailored to at-risk 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".