Well-being approaches targeted to improve child and youth health post-COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
BACKGROUND: Our previous work synthesized published studies on well-being interventions during COVID-19. As we move into a post-COVID-19 pandemic period there is a need to comprehensively review published strategies, approaches, and interventions to improve child and youth well-being beyond deleterious impacts experienced during COVID-19. METHODS: Seven databases were searched from inception to January 2023. Studies were included if they: (1) presented original data on an approach (i.e., approach applied) or (2) provided recommendations to inform development of a future approach (i.e., approach suggested), (3) targeted to mitigate negative impacts of COVID-19 on child and youth (≤18 year) well-being, and (4) published on or after December 2019. RESULTS: 39 studies (n = 4/39, 10.3% randomized controlled trials) from 2021 to 2023 were included. Twenty-two studies applied an approach (n = 22/39, 56.4%) whereas seventeen studies (n = 17/39, 43.6%) suggested an approach; youth aged 13-18 year (n = 27/39, 69.2%) were most frequently studied. Approach applied records most frequently adopted an experimental design (n = 11/22, 50.0%), whereas approach suggested records most frequently adopted a cross-sectional design (n = 13/22, 59.1%). The most frequently reported outcomes related to good health and optimum nutrition (n = 28/39, 71.8%), followed by connectedness (n = 22/39, 56.4%), learning, competence, education, skills, and employability (n = 18/39, 46.1%), and agency and resilience (n = 16/39, 41.0%). CONCLUSIONS: The rapid onset and unpredictability of COVID-19 precluded meaningful engagement of children and youth in strategy development despite widespread recognition that early engagement can enhance usefulness and acceptability of interventions. Published or recommended strategies were most frequently targeted to improve connectedness, belonging, and socialization among children and youth.
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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 | 0.024 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".