The eternal quest for optimal balance between maximizing pleasure and minimizing harm: The compensatory health beliefs model
Bibliographic record
Abstract
Particularly in the health domain, humans thrive to reach an equilibrium between maximizing pleasure and minimizing harm. We propose that a cognitive strategy people employ to reach this equilibrium is the activation of Compensatory Health Beliefs (CHBs). CHBs are beliefs that the negative effects of an unhealthy behaviour can be compensated for, or "neutralized," by engaging in another, healthy behaviour. "I can eat this piece of cake now because I will exercise this evening" is an example of such beliefs. Our theoretical framework aims at explaining why people create CHBs and how they employ CHBs to regulate their health behaviours. The model extends current health behaviour models by explicitly integrating the motivational conflict that emerges from the interplay between affective states (i.e., cravings or desires) and motivation (i.e., health goals). As predicted by the model, previous research has shown that holding CHBs hinder an individual's success at positive health behaviour change, and may explain why many people fail to adhere to behaviour change programs such as dieting or exercising. Moreover, future research using the model and implications for possible interventions are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".