Evaluating an online well-being program for college students during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction: The global COVID-19 pandemic has aggravated challenges involving college students’ mental health and well-being. Some literature suggested developing online programs to address the pandemic’s impact on college students’ mental health and well-being. Thus, this study assessed if significant improvement in well-being among college students can be observed after introducing an online well-being program.Methods: The study utilized a quantitative methodology, mainly using a two-group pretest-posttest design on 178 college students in a private college and state university. The experimental group received 3 months of the well-being program while the control resumed their activities of daily living (ADL). The modified positive emotion, engagement, relationship, meaning, and accomplishment (PERMA) profiler questionnaire was the primary evaluation instrument that measured the participants’ well-being. The first phase gathered the participants’ relevant profile and background, and the last phase concluded with the evaluation of the program. Data were analyzed using SPSS v.21.Results: Based on the post-evaluation PERMA scores, the experimental participants (M = 7.21, SD 1.70) did not differ much from the control (M = 7.07, SD = 1.55) according to a t-test t(176) = –1.07, p = 0.57 as computed using a two-sample independent t-test at a significance level of α = 0.05. The overall PERMA score description is normal functioning. The Pearson correlation of the experimental group’s pre-test and post-test scores (r(91) = 0.01, p = 0.904) and the control (r(83) = 0.04, p = 0.732) group did not indicate an evidence of a significant relationship.Conclusion: The results do not provide evidence of a significant difference and relationship between the experimental participants’ pre-test and post-test PERMA scores after the online well-being program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".