A light in the dark: How children make sense of <scp>COVID</scp>‐19
Bibliographic record
Abstract
Abstract Understanding the negative impact of the pandemic on children and adolescents is essential in order to provide proper support and intervention. Nonetheless, surmounting adversity, such as COVID‐19, may also provide positive lessons for youth to overcome the negative consequences of the pandemic and prepare society for future crises. The objective of the current qualitative study was to document the perceived positive aspects identified by children and adolescents during COVID‐19 and how they made sense of their experience. Participants (N = 67, 5–14 years old) were recruited in May and June 2020. Semi‐structured interviews were conducted via a videoconferencing platform. Based on the transcribed and coded interviews, a thematic qualitative analysis was derived utilizing NVivo. Participants' answers were grouped into four main themes and sub‐themes: (1) school changes, (2) bonding time, (3) free time, and (4) technology usage. Analysing youth perspectives on their experience of the COVID‐19 pandemic provides insight into some of the positive changes and lessons that can be gained amidst the overwhelming negative consequences of the pandemic.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".