Self‐control in daily life: Prevalence and effectiveness of diverse self‐control strategies
Bibliographic record
Abstract
OBJECTIVE: What strategies do people use to resist desires in their day-to-day life? How effective are these strategies? Do people use different strategies for different desires? This study addresses these questions using experience sampling to examine strategy use in daily life. METHOD: = 20.4, 63% female) reported on their use of six specific strategies (situation modification, distraction, reminding self of goals, promise to give in later, reminder of why it is bad, willpower) to resist desires (4,462 desires reported over a week). RESULTS: Participants reported using at least one strategy 89% of the time, and more than one strategy 25% of the time. Goal reminders and promises to give in later were more likely to be used for stronger desires. People also preferred different strategies for different types of desires (e.g., eating vs. leisure vs. work, etc.). CONCLUSION: In contrast to recent theoretical predictions, we find that many strategies, including inhibition, are similarly effective and that using multiple strategies is especially effective.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".