Strategies to mitigate the impact of the COVID-19 pandemic on child and youth well-being: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Children and youth are often more vulnerable than adults to emotional impacts of trauma. Wide-ranging negative effects (eg, social isolation, lack of physical activity) of the COVID-19 pandemic on children and youth are well established. This scoping review will identify, describe and categorise strategies taken to mitigate potentially deleterious impacts of the COVID-19 pandemic on children, youth and their families. METHODS AND ANALYSIS: We will conduct a scoping review following the Arksey-O'Malley five-stage scoping review method and the Scoping Review Methods Manual by the Joanna Briggs Institute. Well-being will be operationalised according to pre-established domains (health and nutrition, connectedness, safety and support, learning and competence, and agency and resilience). Articles in all languages for this review will be identified in CINAHL, Cochrane CENTRAL Register of Controlled Trials, EMBASE, ERIC, Education Research Complete, MEDLINE and APA PsycINFO. The search strategy will be restricted to articles published on or after 1 December 2019. We will include primary empirical and non-empirical methodologies, excluding protocols, reports, opinions and editorials, to identify new data for a broad range of strategies to mitigate potentially deleterious impacts of the COVID-19 pandemic on child and youth well-being. Two reviewers will calibrate screening criteria and the data abstraction form and will independently screen records and abstract data. Data synthesis will be performed according to the convergent integrated approach described by the Joanna Briggs Institute. ETHICS AND DISSEMINATION: Ethical approval is not applicable as this review will be conducted on published data. Findings of this study will be disseminated at national and international conferences and will inform our pan-Canadian multidisciplinary team of researchers, public, health professionals and knowledge users to codesign and pilot test a digital psychoeducational health tool-an interactive, web-based tool to help Canadian youth and their families address poor mental well-being resulting from and persisting beyond the COVID-19 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 | 0.158 | 0.142 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.022 |
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".