Exploring the Impact of an Open Access Mindfulness Course with Online Graduate Students: A Mixed Methods Explanatory Sequential Study
Bibliographic record
Abstract
As enrollment in online graduate education increases, retention continues to be problematic for many colleges and universities across the United States. Non-traditional students, who represent the majority of online graduate student enrollment, have unique issues related to persistence considering they often must juggle the demands of graduate school with work and families. The competing demands can lead to increased levels of perceived stress, which can impact academic performance due to increased mind wandering and decreased attention. Mindfulness is a practice that has been shown in the literature to decrease levels of perceived stress and mind wandering, therefore, the integration of mindfulness practice could have a positive effect on student persistence in online graduate education. Therefore, an online open access mindfulness course was created at one large urban university. The purpose of this explanatory sequential study was to explore the impact of teaching mindfulness to online graduate students. Self-report levels of perceived stress and mind wandering were significantly lower after students completed Module One of an open access mindfulness course. Self-reported perceived persistence levels were found to be significantly higher after Module One with students in the first or second quarter of their program, students with little or no mindfulness experience, and students who meditated four or more times a week. Furthermore, students interviewed felt that the course provided excellent foundational information about mindfulness that could be immediately applied, and therefore should be a requirement for all incoming 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".