On the nature and function of scoring protocols used in eating motivation research: An empirical study of the Regulation of Eating Behaviour Scale
Bibliographic record
Abstract
Objective: The purpose of this study was to examine the effects of different scoring protocols used with the Regulation of Eating Behaviour Scale (REBS; Pelletier et al., 2004). The REBS is a 24-item self-report instrument designed to assess motives that regulate healthy eating behaviour using Organismic Integration Theory (Deci & Ryan, 2002) as a guiding framework. Methods: A multi-cohort investigation was undertaken including a 6-month prospective study (N = 246; Higher education staff/employees), a cross-sectional study (N = 377; Philippine university students/staff), and an internet-based daily diary study (N = 82; Weight-monitoring program attendees). Dietary intake was assessed using self-report instruments or digital monitors. All cohorts completed the REBS. Results: Effect sizes varied with the use of different REBS scoring protocols (R2adj. = 0.02-0.35) whereby models using the Relative Autonomy Index or Autonomous/Controlled motives accounted for less variance than item-aggregation models. Integrated regulation was the strongest correlate of fruit/vegetable (r12 = 0.27-0.56) and reduced fat (r12 = -0.20) consumption. Discussion: Self-determined yet extrinsic motives for healthy eating appear to be important resources that regulate dietary intake. Scoring protocols used in eating behaviour research with the REBS represent a key consideration for OIT-based studies attempting to disentangle the motivational basis of healthy eating.Acknowledgments: Funding provided by the SSHRC & BUAF
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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 | 0.202 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".