The Impact of SARS-CoV-2 (COVID-19) on the Acuity of Mental Health–Related Diagnosis at Admission for Young Adults in New York City and Washington, DC: Observational Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has required restrictive measures to mitigate transmission of the virus. Evidence has demonstrated increased generalized anxiety and depression among young adults due to the COVID-19 pandemic. However, minimal research has examined the longitudinal effect of COVID-19 over the course of time and its impact on anxiety and depression. Additionally, age and gender have been found to play a significant role on individuals' mental health, with young adults and women particularly at risk. OBJECTIVE: The aim of this study was to examine the impact of the COVID-19 pandemic on anxiety and depression upon admissions to treatment. METHODS: This was an observational study that was completed longitudinally in which the grouping variable split the time interval into five equal groups for assessments over each period of time. A total of 112 young adults (aged 18-25 years) were recruited for the study. Participants completed assessments online through a Qualtrics link. RESULTS: Psychometric properties of the admission assessments were uniformly highly statistically significant. There was a significant difference in generalized anxiety between the group-1 and group-3 time intervals. No significant difference was found across the time intervals for depression. Differences in predicting the impact of the psychometrics scores were found with respect to gender. Only the ability to participate and the quality-of-life subfactor of the Functional Assessment of Chronic Illness Therapy (FACIT) assessment were significant. CONCLUSIONS: This study sought to understand the impact that COVID-19 has had on young adults seeking mental health services during the pandemic. Gender emerged as a clear significant factor contributing to increased anxiety in young adults seeking mental health services during the pandemic. These findings have critical importance to ensuring the potential treatment success rate of clients, while providing an overarching understanding of the impact of the pandemic and establishing clinical recommendations for the treatment of individuals who are seeking out treatment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".