International Inequity Patterns in Youth and Young Adults Related to COVID-19: Advancing Sustainable Development Goals on Well-Being, Education, and Employment
Bibliographic record
Abstract
COVID-19 has threatened physical and mental health and reduced overall wellbeing. Wellbeing has declined significantly since the pandemic, particularly within younger sub-populations aged 15-29. Past disasters and current crises indicate that decreased wellbeing can have serious long-term health, social, and financial effects for the individual and society. This is particularly concerning for younger people whose lives and livelihoods are developing. Research indicates that supports put in place at younger ages typically lead to multiple gains in growth and development. Our objective was to use a scoping review spanning 2020-2021 to map the patterns of international inequity on young people in terms of education, employment, and mental health. As educators, we note the special capacity within the United Nations sustainable development goals to address these challenges. With its inherent focus on wellbeing, the sustainable development goals framework can be used to guide the strategic processes to address wellbeing, especially in these vulnerable subpopulations. The review enabled us to identify preliminary steps for strategy development related to policy and for educators to consider in supporting youth and young adults. Many universities have established processes for advancing sustainable development goals. Educators’ expertise and capacity to effect change can be harnessed to improve mental health and wellbeing locally while contributing to longer-term sustainable development within society. The authors stated that wellbeing is a beacon for the future. Monitoring wellbeing and taking steps to address its decline, especially in the young, must be a priority as they are the future leaders of society. Youth and young adults will also bear the brunt of the economic and social burden due to the pandemic. By investing in the wellbeing of today’s youth and young adults there are opportunities to «pay it forward» so that better outcomes may be realized in the future.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".