The Impact of COVID-19 on Access and Uptake of Children and Youth Mental Health Care Services
Bibliographic record
Abstract
Background: There has been a greater demand for child and adolescent mental health services following the onset of the COVID-19 pandemic. We aimed to examine the impact of the pandemic and introduction of virtual mental healthcare (VMHC) on service utilization patterns among a diverse cohort of youth (ages 6-18).Methods: Archival data were obtained from a multisite community clinic between January 2018 and March 2022, totalling 26,422 client contacts. Differences in service utilization patterns and frequency of marginalized youth served were analyzed using independent samples t-tests. Chi square tests were used to determine differences in age groups served, and family member attendance.Results: Relative to pre-COVID, there were significant increases in the number of intervention sessions and the age of clients served post-COVID; there were no significant differences in the number of clients served or frequency of no-shows per month. There were significant decreases across three of the four dimensions of marginalization studied (deprivation, dependency, and ethnic concentration). There were no significant differences in service utilization patterns during the post-COVID school closure compared to post-COVID school operational period. There was a significant increase in both parental involvement in-session and age of clients who were served through VMHC. Conclusions: The COVID-19 pandemic was associated with a significant increase in the number of clinical encounters. VMHC allowed for greater incorporation of family members in-session, though it did not translate to serving more marginalized populations. Further research is required to explore the specific barriers impeding virtual care accessibility amongst marginalized communities.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".