Bibliographic record
Abstract
Undergraduate students struggle to incorporate stress coping strategies or self care routines into their lives that take time (e.g., yoga or exercise). However, there are many useful stress coping methods that do not require a significant time investment (e.g., deep breathing, cognitive reappraisal). These strategies can also be employed in the moment when students are facing an immediate stressor, like writing a final exam. Although these simple strategies exist, students often do not use them in the moment. It is unclear whether this lack of use is due to lack of knowledge , lack of belief that the strategy will be effective, or other factors. Our proposed study will examine these first two ideas. We will recruit participants into one of four groups: 1) deep breathing, 2) cognitive reappraisal, 3) deep breathing with biofeedback, 4) cognitive reappraisal with biofeedback. Physiological (i.e., heart rate variability) and psychological (i.e., perceived stress) measures will be assessed at baseline. Participants will then complete a stress-inducing mathematics task. Next, participants will be taught one of the two stress interventions. The stress task will then be repeated, and participants will be asked to use the intervention during the second stress task, either receiving heart rate variability biofeedback or not. Throughout the rest of the semester, participants will receive email prompts inquiring if they have used the learned intervention. We hypothesize that participants who receive biofeedback, and thus see that the intervention is effective, will use the intervention more often throughout the semester. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Michele Moscicki Department: Psychology
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".