Effectiveness of Online Positive Psychology Intervention on Psychological Well-Being Among Undergraduate Students
Bibliographic record
Abstract
Positive psychology intervention is mediation that aims to promote quality of life and well-being. Current research integrating positive psychology with the Internet is called online positive psychology (OPP) which promotes and prevents mental health problems, improves well-being, and reduces depression. This experimental research aimed 1) to compare the psychological well-being of the experimental group that received online positive psychology intervention in the phase of pre-test, post-test, and follow up and 2) to compare the psychological well-being between the experimental group and the controlled group. The subjects were 24 undergraduate students from Mahasarakham University, Thailand, selected by purposive sampling. Thereafter, the subjects were equally divided into experimental and controlled groups. Measures used in this study were as follows: 1) the online positive psychology intervention to improve psychological well-being and 2) the scale of psychological well-being based on Ryff’s psychological well-being. The statistics used in the data analysis were the Friedman Test, Wilcoxon signed-rank test, and the Mann Whitney U Test. The results of the pre-test and the post-test showed that the mean scores of psychological well-being of the experimental group were significantly different at 0.05 levels. Additionally, the mean scores of psychological well-being between the experimental group and the controlled group in the phases of post-test and follow-up were significantly different at 0.05 levels. The online positive psychology intervention was effective in increasing the psychological well-being of undergraduate students.
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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 | 0.001 | 0.002 |
| 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) | 0.004 | 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".