Challenges and Opportunities in Mental Health Care forTransitional Age Youth (TAY): Lessons Learned from theCOVID-19 Pandemic
Bibliographic record
Abstract
Abstract: COVID-19, caused by the SARS-CoV-2 virus, began in late 2019. On March 11th, 2020, the World Health Organization (WHO) declared the virus a pandemic, and by March 13th, 2020, the United States (US) government declared the pandemic a national emergency and, subsequently, a global pandemic. Since then, the world has experienced an upheaval affecting every age group in almost every aspect of life. This was particularly true for young people. Globally, a number of studies indicated that the COVID-19 pandemic has had a significant impact on youth mental health measures. In the US, the Centers for Disease Control and Prevention (CDC) 2022 report showed that more than a third of high school students reported that they experienced poor mental health during the pandemic, and 44 percent reported feeling persistently sad or hopeless in the last 12 months. More than half (55 percent) reported experiencing emotional abuse by a parent or other adult at home, and 11 percent reported experiencing physical abuse. In addition, more than a quarter (29 percent) reported that a parent or other adult in the home lost their job. The pandemic upended not only daily life but also healthcare delivery throughout the world. Its vast impact on the healthcare system has disproportionately affected transitional age youth (TAY) between 15 to 25 years old. While the pandemic caused unprecedented changes and challenges, it was also a learning experience that deepened our understanding of our system of care, its strengths and vulnerabilities. This paper will discuss many challenges and opportunities associated with the delivery of psychiatric services for TAY in college mental health, emergency departments, and inpatient and outpatient settings, as well as in specialized programs, such as those for early psychotic disorders.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.015 |
| 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".