Understanding Food Literacy Intervention Effectiveness: Postsecondary Students’ Perspectives on How a mHealth Food Literacy Intervention Impacted Their Dietary Behaviors
Bibliographic record
Abstract
OBJECTIVE: To explore postsecondary students' perspectives of the impacts of a mobile health (mHealth) food literacy intervention on dietary behaviors and why the intervention was or was not effective at influencing their dietary behavior. DESIGN: Qualitative study using semistructured focus groups. SETTING: Ontario, Canada. PARTICIPANTS: Ten focus groups were conducted with postsecondary students (n = 30) aged 17-25 years from 2 universities. PHENOMENON OF INTEREST: The impacts of a mHealth food literacy intervention on participants' dietary behaviors and why the intervention was or was not effective. ANALYSIS: Focus group data were transcribed verbatim and analyzed using inductive thematic analysis. RESULTS: Themes regarding dietary impacts included increased dietary consciousness, decrease in perceived unhealthy foods, increase in perceived healthy foods, making healthier dietary choices, and the Hawthorne effect. Intervention effectiveness themes encompassed barriers and facilitators to engagement and participants' ability to implement the intervention into their dietary behaviors. Facilitators included intervention suitability and application functionality; barriers included technology concerns, lacking time, food accessibility, food affordability, and intervention suitability. CONCLUSION AND IMPLICATIONS: This study provides insights into the impact, facilitators, and barriers of a mHealth food literacy intervention on postsecondary students' dietary behaviors. Consideration of these facilitators and barriers may improve the effectiveness of future interventions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".