An online optimism intervention reduces depression in pessimistic individuals.
Bibliographic record
Abstract
OBJECTIVE: Interest in online positive psychology interventions (OPPIs) continues to grow. The empirical literature has identified design factors (e.g., variety and duration of activities) and moderators (e.g., personality traits) that can influence their effectiveness. A randomized controlled trial tested an empirically informed OPPI designed to promote self-efficacy and an optimistic outlook. Pessimism was included as a trait moderator. METHOD: Participants (N = 466) were English-speaking adults interested in becoming happier. They were randomly assigned to complete either an OPPI cultivating optimism or a control condition writing about daily activities for 3 weeks. Follow-up assessments occurred 1 and 2 months following the exercise period. RESULTS: A hierarchical linear model analysis indicated that the optimism intervention increased the pursuit of engagement-related happiness in the short term and reduced dysfunctional attitudes across follow-ups. Pessimistic individuals had more to gain and reported fewer depressive symptoms at post-test. CONCLUSIONS: These findings support the conclusion that empirically informed online interventions can improve psychological well-being, at least in the short run, and may be particularly helpful when tailored to the needs of the individual.
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 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".