Do Positive Psychology Exercises Work? A Replication of Seligman et al. ()
Bibliographic record
Abstract
OBJECTIVES: The current work replicated a landmark study conducted by Seligman and colleagues (2005) that demonstrated the long-term benefits of positive psychology exercises (PPEs). In the original study, two exercises administered over 1 week ("Three Good Things" and "Using your Signature Strengths in a New Way") were found to have long-lasting effects on depression and happiness (Seligman, Steen, Park, & Peterson, 2005). DESIGN: These exercises were tested here using the same methodology except for improvements to the control condition, and the addition of a second "positive placebo" to isolate the common factor of accessing positive, self-relevant constructs. This component control design was meant to assess the effect of expectancies for success (expectancy control), as well the cognitive access of positive information about the self (positive placebo). RESULTS: Repeated measures analyses showed that the PPEs led to lasting increases in happiness, as did the positive placebo. The PPEs did not exceed the control condition in producing changes in depression over time. CONCLUSIONS: Brief, positive psychology interventions may boost happiness through a common factor involving the activation of positive, self-relevant information rather than through other specific mechanisms. Finally, the effects of PPEs on depression may be more modest than previously assumed.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".