Applying the self‐determination theory continuum to unhealthy eating: Consequences on well‐being and behavioral frequency
Bibliographic record
Abstract
Abstract The present research applies self‐determination theory (SDT) to the context of unhealthy eating. The extent to which each of the six types of motivations stemming from the SDT continuum applies to unhealthy eating is examined, as well as the contribution of each motivation for eating unhealthily in predicting psychological well‐being and frequency of unhealthy eating. A three‐wave longitudinal study (N = 379) was conducted before, during, and after the Christmas holidays. Results demonstrated that three types of motivations (i.e., identified, introjected, and external regulation) for unhealthy eating fluctuated over time and peaked during the holidays, a time when unhealthy eating becomes especially salient in Western societies. However, amotivation for unhealthy eating reached its lowest level during the holidays. While identified regulation was associated with greater well‐being, introjected regulation, and amotivation for unhealthy eating were linked with lower well‐being. Integrated regulation was associated with lower well‐being only before and after the holiday period. Finally, the integrated, introjected, external regulations, and amotivation were linked to higher frequency of unhealthy eating. These results confirm that each type of motivation presents distinct patterns of associations with well‐being and actual behavior. Results also demonstrate that the social context in which eating takes place can have an impact on the relationship between motivations and well‐being as well as behavior.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".