Adolescent Development in Unprecedented Times: A Comparative Study of Theory of Mind Before and After COVID-19
Bibliographic record
Abstract
The coronavirus disease of 2019 (COVID-19) pandemic represented a significant global event that disrupted the typical social lives of adolescents. Studies show that the pandemic negatively impacted adolescent well-being. Very little is known about the social–cognitive implications of wide-scale social distancing, school closures, and the transition to digital modes of communication for adolescents. This study aims to compare affective Theory of Mind (ToM), self-esteem, and self-perceptions before and after the COVID-19 pandemic. Drawing on existing data, this study compared two distinct but comparable samples of adolescents. Data from the pre-COVID-19 sample were collected in the 2016/2017 school year (N = 145, 60.7% female and 39.3% male, mean age 13.38 years. Data from the post-COVID-19 sample were collected over the 2021/2022 school year (N = 107 participants, 51% female, 48% male, and 1% genderqueer, mean age of 13 years). Independent-sample t-tests and two-tailed correlational analysis were used to compare associations and changes in affective ToM, self-perceptions, and self-esteem between the pre-COVID-19 and post-COVID-19 samples. The main findings of this study includes declines in affective ToM post-COVID-19, specifically related to the perception of negative affect. Further, perceived self-worth and behavioural conduct declined after the COVID-19 pandemic. Implications for social–emotional learning and future research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".