Optimists and Realists: A Latent Class Analysis of Students Graduating from High School during COVID-19 and Impacts on Affect and Well-Being
Bibliographic record
Abstract
The Novel Coronavirus Disease (COVID-19) pandemic has had profound effects on physical and mental health worldwide. Students transitioning out of high school were uniquely impacted at the onset of the pandemic, having missed the opportunity to properly mark the end of their final year in the K-12 school system. The adverse effects of this loss on this population are still unknown. The purpose of the current study was to examine stress, wellbeing, and affect in a sample of 168 students (N = 168; Mage = 17.0, SD = 0.46; 60% female; 40% male) who were completing their final year of high school during the early stages of the pandemic when emergency stay-at-home orders were in place. Participants completed an online survey assessing the impact of COVID-19 on their life satisfaction (pre-COVID19, during COVID-19, and anticipated five years from now), stress, positive affect, and negative affect. Latent class analysis (LCA) was used to create classes of participants based on their responses to the pandemic. A two-subgroup solution provided the best model for the life satisfaction outcome variable. Subgroup 1, optimists, comprised 24% (N = 40) of the sample and reported high life satisfaction ratings one year prior to COVID-19 and a slight decrease in life satisfaction during COVID-19, and they anticipated an increase in life satisfaction 5 years from now. This group was characterized by low stress, low negative affect, and high positive affect during the pandemic. Subgroup 2, realists, comprised 76% of the population (N = 128) and experienced similarly high retrospective ratings of pre-COVID life satisfaction but a larger decrease in life satisfaction during the pandemic and a smaller increase in five years. The realist group was characterized by low positive affect, high stress, and high negative affect during the pandemic. The findings suggest that during the pandemic, certain subsamples of adolescents had greater difficulty in managing this transitional period and experienced changes in mood and well-being (i.e., affect, stress) as compared to other adolescents (i.e., optimists). Future research should investigate the characteristics and coping mechanisms that are instrumental for increasing life satisfaction and positive affect while lowering stress in this population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".