Discrete-Time Bayesian Survival Analysis: The Impact of Stress Management Courses
Bibliographic record
Abstract
Students fail to complete undergraduate degrees from universities for a variety of reasons. Although many factors exist that impact a student???s decision to leave university, common factors include stress, depression, burnout, anxiety, work, and financial difficulties. Some students may decide to stop out from university, putting their education on hold with the intention of returning and completing a degree. While universities offer health and counseling services to address students??? physical and mental well-being, many students do not seek help from counseling services when they need it. At Cal Poly Pomona, a growing number of undergraduate students have enrolled in stress management courses offered through the Department of Kinesiology and Health Promotion. In this thesis, we will use Bayesian survival analysis to assess the impact that a stress management course has on graduation rates by comparing students who enrolled in the stress management course to those who did not enroll in the course. Results show that students who enroll in the course attain graduation rates higher than those who do not take the course, particularly students who enroll during the early quarters of junior year. Additionally, students who enroll in the stress management course the first quarter of their junior year attain three- and four-year graduation rates higher than students who never enroll in the course when taking cumulative grade point average into account. It is important to note that the association between taking stress management courses and graduation rates should not be interpreted as a causal relationship between the two events.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".