Associations Between Emodiversity and Mental Health in University Students During the COVID-19 Pandemic
Bibliographic record
Abstract
Emodiversity refers to the breadth and scope of emotions a person experiences day to day and may be uniquely related to mental health mean levels of positive and negative emotion. We examined the associations between positive and negative emodiversity, mean positive and negative emotion, and three mental health indicators: depressive symptoms, anxious symptoms, and overall wellbeing in a sample of undergraduate students (N = 592, 80% women, mode of age = 20 years) during different phases of lockdown during the COVID-19 pandemic. Participants completed a 14-day daily diary survey to assess their daily positive and negative emotions. Results indicated significant interactions between negative emodiversity and mean levels of negative mood in predicting symptoms of depression and anxiety and overall wellbeing. Specifically, for individuals who reported greater mean levels of negative mood, low negative emodiversity was associated with greater depressive and anxious symptoms and lower wellbeing. Results for positive emodiversity were not significant. These associations did not differ across changing pandemic restrictions. Results suggest that rigidity in negative emotions in daily life (i.e., high levels of negative emotion with low diversity in negative emotion states) are an important feature of mental health and wellbeing among university 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".