Régulation de la prise alimentaire consécutive à un travail mental exigeant.
Bibliographic record
Abstract
Knowledge-based work has been identified as a potential factor that might accentuate positive energy balance and weight gain. The reasons explaining this relationship and the gender differences previously observed are still unknown. To investigate the relationships between mental effort, cognitive restraint and motivation based on the Strength Model of Self-Regulation, in order to predict eating regulation following a demanding mental work. The protocol consists of a randomized crossover design including 3 conditions (knowledge-based work, exercise and control) followed by an ad libitum buffet measuring eating regulation. Mental effort is measured by the average reaction time (RT) to a second mental task. Questionnaires were administrated at baseline to evaluate global motivation and eating behaviour traits. Cognitive restraint, motivation and mental effort variables do not significantly moderate the relationship between experimental conditions and eating regulation. Controlled form of motivation is significantly correlated with flexible restraint, r = .3, p = .04, rigid restraint, r = .4, p = .03 and disinhibition, r = .3, p = .047. Controlled motivational variable is associated to behaviour traits likely to impair adequate eating regulation, but these associations do not seem to moderate acute food intake regulation following knowledge-based work. Knowledge-based work and flexible cognitive restraint independently increase mental effort, having the potential to weaken self-regulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".