Loneliness and Well-Being in Children and Adolescents during the COVID-19 Pandemic: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Concerns have been raised about the loneliness and well-being of children and adolescents during the COVID-19 pandemic. The extent to which the ongoing pandemic has impacted loneliness and the association between loneliness and well-being is unclear. Therefore, a systematic review of empirical studies on the COVID-19 pandemic was conducted to examine the (1) prevalence of loneliness in children and adolescents, (2) associations between loneliness and indicators of well-being, and (3) moderators of these associations. Five databases (MEDLINE, Embase, PsycInfo, Web of Science, ERIC) were searched from 1 January 2020 to 28 June 2022 and 41 studies met our inclusion criteria (cross-sectional: n = 30; longitudinal: n = 11; registered on PROSPERO: CRD42022337252). Cross-sectional prevalence rates of pandemic loneliness varied, with some finding that over half of children and adolescents experienced at least moderate levels of loneliness. Longitudinal results reflected significant mean increases in loneliness compared to pre-pandemic levels. Cross-sectional results indicated that higher levels of loneliness were significantly associated with poorer well-being, including higher depression symptoms, anxiety symptoms, gaming addiction, and sleep problems. Longitudinal associations between loneliness and well-being were more complex than cross-sectional associations, varying by assessment timing and factors in the statistical analyses. There was limited diversity in study designs and samples, preventing a thorough examination of moderating characteristics. Findings highlight a broader challenge with child and adolescent well-being that predates the pandemic and the need for future research to examine underrepresented populations across multiple timepoints.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it