Impact of COVID-19 on psychoactive medication use among individuals with intellectual and developmental disabilities in Ontario, Canada: A repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Evidence for worsening mental health among individuals with intellectual and developmental disabilities (IDD) during COVID-19 sparked concerns for increased use of psychoactive medications. OBJECTIVE: To examine the impact of COVID-19 on psychoactive medication use and clinical monitoring among individuals with IDD in Ontario, Canada. METHODS: We conducted a repeated cross-sectional study among individuals with IDD and examined weekly trends for psychoactive medication dispensing and outpatient physician visits among those prescribed psychoactive medications between April 7, 2019, and March 25, 2023. We used interventional autoregressive integrated moving average models to determine the impact of the declaration of emergency for COVID-19 (March 17, 2020) on the aforementioned trends. RESULTS: The declaration of emergency for COVID-19 did not significantly impact psychoactive medication use among individuals with IDD. Provision of clinical monitoring remained relatively stable, apart from a short-term decline in the weekly rate of outpatient physician visits following the declaration of emergency for COVID-19 (step estimate: 21.26 per 1000 individuals [p < 0.01]; ramp estimate: 0.88 per 1000 individuals [p = 0.01]). When stratified by mode of delivery, there was a significant shift towards virtual care (step estimate: 78.80 per 1000 individuals; p < 0.01). The weekly rate of in-person physician visits gradually increased, returning to rates observed prior to the COVID-19 pandemic in January 2023. CONCLUSION: Although access to clinical care remained relatively stable, the shift towards virtual care may have negatively impacted those who encounter challenges communicating via virtual mediums. Future research is required to identify the support systems necessary for individuals with IDD during virtual health care interactions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".