Associations over the COVID-19 pandemic period and the mental health and substance use of youth not in employment, education or training in Ontario, Canada: a longitudinal, cohort study
Bibliographic record
Abstract
BACKGROUND: The economic shutdown and school closures associated with the COVID-19 pandemic have negatively influenced many young people's educational and training opportunities, leading to an increase in youth not in education, employment, or training (NEET) globally and in Canada. NEET youth have a greater vulnerability to mental health and substance use problems, compared to their counterparts who are in school and/or employed. There is limited evidence on the association between COVID-19 and NEET youth. The objectives of this exploratory study included investigating: longitudinal associations between the COVID-19 pandemic and the mental health and substance use (MHSU) of NEET youth; and MHSU among subgroups of NEET and non-NEET youth. METHODS: 618 youth (14-28 years old) participated in this longitudinal, cohort study. Youth were recruited from four pre-existing studies at the Centre for Addiction and Mental Health. Data on MHSU were collected across 11 time points during the COVID-19 pandemic (April 2020-August 2022). MHSU were measured using the CoRonavIruS Health Impact Survey Youth Self-Report, the Global Appraisal of Individual Needs Short Screener, and the PTSD Checklist for DSM-5. Linear Mixed Models and Generalized Estimating Equations were used to analyze associations of NEET status and time on mental health and substance use. Exploratory analyses were conducted to investigate interactions between sociodemographic characteristics and NEET status and time. RESULTS: At baseline, NEET youth were significantly more likely to screen positive for an internalizing disorder compared to non-NEET youth (OR = 1.92; 95%CI=[1.26-2.91] p = 0.002). No significant differences were found between youth with, and without, NEET in MHSU symptoms across the study time frame. Youth who had significantly higher odds of screening positive for an internalizing disorder included younger youth (OR = 1.06, 95%CI=[1.00-1.11]); youth who identify as Trans, non-binary or gender diverse (OR = 8.33, 95%CI=[4.17-16.17]); and those living in urban areas (OR = 1.35, 95%CI=[1.03-1.76]), compared to their counterparts. Youth who identify as White had significantly higher odds of screening positive for substance use problems (OR = 2.38, 95%CI=[1.72-3.23]) compared to racialized youth. CONCLUSIONS: Our findings indicate that sociodemographic factors such as age, gender identity, ethnicity and area of residence impacted youth MHSU symptoms over the course of the study and during the pandemic. Overall, NEET status was not consistently associated with MHSU symptoms over and above these factors. The study contributes to evidence on MHSU symptoms of NEET youth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".