The Impact of COVID-19 on Mental Health Outcomes among Recipients of Ontario Social Assistance Benefits
Bibliographic record
Abstract
Objectives/ApproachWe linked the Ontario Social Assistance (OSA) Benefit Unit file from the Ministry of Children, Community and Social Services to provincial health administrative datasets housed at ICES to examine the impact of COVID-19 on Mental Health and Addictions (MHA) service use among OSA recipients. Those receiving OSA benefits for February 2020 were matched to Ontario residents not receiving benefits based on age (±1 year), sex, income, expected resource utilization, and area of residence (N=771,891 matched pairs). We computed rates of MHA-related emergency department (ED) visits and hospitalizations in the 16-month period before (November 2018-February 2020) and after (March 2020-June 2021) pandemic onset. ResultsMHA-related ED visit rates were much greater among OSA recipients (8.82 per 1,000 person-months) compared with matched controls (1.83 per 1,000 person-months) in the post-COVID period (Relative Rate [RR]=4.82; 95% Confidence Interval [95%CI] 4.75-4.89). Comparatively, MHA-related ED visit rates were also greater among OSA recipients (9.85 per 1,000 person-months) compared with controls (2.38 per 1,000 person-months) during the pre-COVID period (RR=4.13; 95%CI 4.08-4.18). The pre-COVID and post-COVID period RRs comparing OSA recipients and matched controls were significantly different (p<0.001). Results were consistent when examining MHA-related hospitalizations. Conclusions/ImplicationsWhile MHA-related ED visits and hospitalizations rates were significantly higher in OSA recipients than matched controls in both time periods, this difference increased following the onset of COVID-19. Increased reliance on hospital-based MHA services post-COVID represents the greater ongoing need for, and/or reduced access to, MHA-related ambulatory care for OSA recipients during the pandemic.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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".