Digital and Hybrid Pediatric and Youth Mental Health Program Implementation Challenges During the Pandemic: Literature Review With a Knowledge Translation and Theoretical Lens Analysis
Bibliographic record
Abstract
BACKGROUND: The pandemic brought unprecedented challenges for child and youth mental health. There was a rise in depression, anxiety, and symptoms of suicidal ideation. OBJECTIVE: The aims of this knowledge synthesis were to gain a deeper understanding of what types of mental health knowledge translation (KT) programs, mental health first aid training, and positive psychology interventions were developed and evaluated for youth mental health. METHODS: We undertook a literature review of PubMed and MEDLINE for relevant studies on youth mental health including digital and hybrid programs undertaken during the pandemic (2020-2022). RESULTS: A total of 60 studies were included in this review. A few KT programs were identified that engaged with a wide range of stakeholders during the pandemic, and a few were informed by KT theories. Key challenges during the implementation of mental health programs for youth included lack of access to technology and privacy concerns. Hybrid web-based and face-to-face KT and mental health care were recommended. Providers required adequate training in using telehealth and space. CONCLUSIONS: There is an opportunity to reduce the barriers to implementing tele-mental health in youth by providing adequate technological access, Wi-Fi and stationary internet connectivity, and privacy protection. Staff gained new knowledge and training from the pandemic experience of using telehealth, which will serve as a useful foundation for the future. Future research should aim to maximize the benefits of hybrid models of tele-mental health and face-to-face sessions while working on minimizing the potential barriers that were identified. In addition, future programs could consider combining mental health first aid training with hybrid digital and face-to-face mental health program delivery along with mindfulness and resilience building in a unified model of care, knowledge dissemination, and implementation.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".