The association between local public health unit funding and adolescent mental health during the COVID-19 pandemic: Longitudinal findings from the Ontario public health information database (OPHID) and COMPASS studies
Bibliographic record
Abstract
BACKGROUND: Public health units (PHU) require adequate funding to service the needs of their community. This study examined the association between PHU funding and adolescent mental health trajectories from 2018-19 to 2020-21 in Ontario, Canada. METHODS: Longitudinal adolescent data from the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behaviour (COMPASS) study (n = 6043) was used. Adolescents reported on depression and anxiety symptoms via questionnaire. Data on funding from 12 PHUs was obtained from the Ontario Public Health Information Database. We used multilevel growth curve analyses to estimate whether PHU funding modified trajectories of depressive and anxiety symptoms while adjusting for student, school, and area-level covariates. We also tested for heterogeneity by gender and performed gender stratified analyses. RESULTS: PHU funding modified depression trajectories. Differences in depression scores by level of PHU funding were not significant at each time point. However, students attending schools in areas with lower PHU funding had smaller rates of growth in depression from 2018-19 to 2019-20 (Low PHU: ∂ = 0.50, 95 % CI: 0.27, 0.74; High PHU: ∂ = 1.10, 95 % CI: 0.81, 1.39), but larger rates of growth from 2019-20 to 2020-21 (Low PHU: ∂ = 1.66, 95 % CI: 1.32, 2.00; High PHU: ∂ = 0.70, 95 % CI: 0.35, 1.05). Findings were similar in female stratified models. PHU funding did not modify anxiety trajectories. CONCLUSION: Trajectories of adolescent depression symptoms, particularly females, during the COVID-19 pandemic may have been modified by the level of PHU funding. More research over a longer period and in different jurisdictions is needed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".