A goal-systems perspective on plant-based eating: keys to successful adherence in university students
Bibliographic record
Abstract
OBJECTIVE: To explore adherence to a plant-based diet from the perspective of goals- and motivations-based systems. DESIGN: A cross-sectional, survey-based study was conducted regarding eating patterns, goals and motivations for current eating habits. SETTING: Data were collected using an online survey platform, including the Goal Systems Assessment Battery (GSAB) and other survey tools. PARTICIPANTS: University students were recruited, including thirty-three students reporting successful maintenance of a plant-based diet (Adherents) and sixty-three students trying to adhere to a plant-based diet (Non-adherents). RESULTS: Using GSAB subscale scores, discriminant function analyses significantly differentiated adherents v. non-adherents, accounting for 49·0 % of between-group variance (χ2 (13) = 42·03, P < 0·000). It correctly classified 72·7 % of adherents and 88·9 % of non-adherents. Constructs including value, self-efficacy, planning/stimulus control and positive affect were significant and included in the discriminant function. Logistic regression results suggested that participants who successfully adhered to a plant-based diet were seventeen times more likely to report 'To manage or treat a medical condition' as motivation and almost seven times more likely to report 'To align with my ethical beliefs' as motivation compared with non-adherents. However, these participants were 94 % less likely to report 'To maintain and/or improve my health' as motivation compared with non-adherents. Controlling for motivations, hierarchical logistic regression showed that only planning as part of the GSAB self-regulatory system predicted adherence to a plant-based diet. CONCLUSIONS: Values-based approaches to plant-based diets, including consideration for ethical beliefs, self-efficacy and proper planning, may be key for successful maintenance of this diet long-term.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".