<i>Perceived Facilitators of and Barriers to</i> Healthful Eating Among University Students
Bibliographic record
Abstract
PURPOSE: Photovoice, an innovative qualitative research method in health care, has not been used to its full potential in nutrition/dietetics. We explored the use of Photovoice to determine perceived facilitators of and barriers to healthful eating among university students. METHODS: The study included 28 students enrolled in a 2008 introductory nutrition class. The students participated in a camera orientation session to review ethics and privacy issues. They took photographs and selected two for discussion in a focus group moderated by a graduate student who used a semi-structured facilitation guide. Researchers coded the transcripts, analyzed the pictures and students' written comments about the project, and ensured data trustworthiness through credibility, dependability, confirmability, and transferability of data and methods. RESULTS: Six major themes emerged as facilitators and/or barriers: environment, nutrition knowledge, convenience foods, time, media influence, and food cost. More than one-third of the students thought the study "stimulated their critical thinking." They felt more empowered in sharing their perceptions and "getting their voices heard." CONCLUSIONS: Photovoice was a useful, "motivating," and "engaging" method for research on nutrition knowledge and dietary patterns of university students. Registered dietitians and other health professionals may benefit from the use of the Photovoice method when they are working with students.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".