Identification of Healthy Eating and Active Lifestyle Issues through Photo Elicitation
Bibliographic record
Abstract
PURPOSE: Effective workplace wellness programs, featuring supports for healthy eating and active lifestyle behaviours, have been found to reduce health risks and the associated economic burdens for individuals, organizations, and their communities. As part of a larger study, the purpose of this research was to engage volunteer participants from a university community to identify healthy eating and active lifestyle barriers and supports. METHODS: An ethics-approved, action-research design with photo elicitation technique was used to engage employees and students. Data were analyzed using qualitative analysis software. RESULTS: Participants identified barriers and both current and future supports for healthy eating and active lifestyle on campus. These were coded under the sub-themes of food environment, food and nutrition quality, physical environment, physical activity, fitness centre, and awareness/communication. CONCLUSION: Photo elicitation was determined to be an effective technique to engage participants. Despite many supports, members of the university community still found it difficult to follow healthy eating and active lifestyle behaviours; however, a number of practical future supports were identified. This study also provided valuable insight into the role that dietitians can play in the development of successful wellness programs.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".