Psychological Interventions and Those With Elements of Positive Psychology for Child and Youth Mental Health During the COVID-19 Pandemic: Literature Review, Lessons Learned, and Areas for Future Knowledge Dissemination
Bibliographic record
Abstract
BACKGROUND: There was a marked decline in child and teenage mental health worldwide during the pandemic, with increasing prevalence of depression, anxiety, and suicide. Research indicates that positive psychological interventions may be beneficial for mental health. OBJECTIVE: The aims of this review were to evaluate positive psychological interventions for child and youth mental health implemented during the COVID-19 pandemic and assess overall effectiveness for mental health and knowledge. METHODS: We undertook a literature search of PubMed, MEDLINE, and Google Scholar for all eligible studies on digital and hybrid in-person psychological interventions for youth mental health during the COVID-19 pandemic. A particular emphasis was placed on positive psychological interventions or interventions that had components of positive psychology, including gratitude, acceptance, positive emotions, or resilience building. RESULTS: A total of 41 interventions were included in this review. Most of the interventions were digital. Overall, most of the interventions assisted with one or more mental health or psychological indicators, such as depression, anxiety, posttraumatic stress disorder, stress, and resilience. However, findings were mixed when it came to targeting both depression and anxiety together. The interventions that promoted youth mental health most often had a range of diverse positive psychology components and were evidence based. Not all studies measured changes in mindfulness. Few studies examined knowledge acquired on mental health self-care, managing mental health problems, knowledge of positive psychological techniques, mindfulness knowledge, or mental health self-efficacy. CONCLUSIONS: Diverse multicomponent interventions appear to assist with youth mental health overall, although their effects on both depression and anxiety are less clear. There is also a need for more research on knowledge gains to determine whether the interventions improved knowledge on mental health-supportive behaviors, which may be sustained beyond the intervention. Finally, more studies need to evaluate whether the interventions assisted with increasing self-efficacy for practicing positive psychological techniques as well as changes in mindfulness levels. Future studies should not only assess effectiveness for mental health outcomes but also assess knowledge translation, with valid measures of knowledge and self-efficacy for mental health-supportive behaviors and positive psychological skills acquired (eg, the ability to practice mindfulness).
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".