Testing the Effectiveness of a Strengths-Based Intervention Targeting All 24 Strengths: Results From a Randomized Controlled Trial
Bibliographic record
Abstract
This study assessed the effectiveness of a strengths-based randomized controlled trial focused on fostering all 24 character strengths in a group of 75 participants from a University in Tunisia. Participants randomly assigned to the challenge condition (n = 40) received an email each day for 24 days, that highlighted a particular strength of the day including why the strength is valuable, how to implement the strength behaviourally, and a motto related to that strength. Those in the control condition (n = 35) simply received emails containing the motto for each strength daily for 24 days. We assessed all participants' levels of happiness before the experiment (T0), the day following the experiment (T1), and one-month following the experiment (T2). Results from a 2 (group) X 3 (time) split plot ANOVA revealed a significant group-by-time interaction, such that at T2 the experimental group had greater happiness scores than the control group. These findings provide some evidence that even "minimalist" interventions (involving the receipt of emails encouraging character-strength development), might be effective for promoting gains in happiness even one month after the intervention.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".