Temporal Trends in Mental Health Service Utilization across Outpatient and Acute Care Sectors: A Population-Based Study from 2006 to 2014
Bibliographic record
Abstract
Objective: Although evidence suggests that treatment seeking for mental illness has increased over time, little is known about how the health system is meeting the increasing demand for services. We examined trends in physician-based mental health service use across multiple sectors. Method: In this population-based study, we used linked health-administrative databases to measure annual rates of mental health–related outpatient physician visits to family physicians and psychiatrists, emergency department visits, and hospitalizations in adults aged 16+ from 2006 to 2014. We examined absolute and relative changes in visit rates, number of patients, and frequency of visits per patient, and assessed temporal trends using linear regressions. Results: Among approximately 11 million Ontario adults, age- and sex-standardized rates of mental health–related outpatient physician visits declined from 604.8 to 565.5 per 1000 population over the study period ( P trend = 0.04). Over time, the rate of visits to family physicians/general practitioners remained stable ( P trend = 0.12); the number of individuals served decreased, but the number of visits per patient increased. The rate of visits to psychiatrists declined ( P trend < 0.001); the number of individuals served increased, but the number of visits per patient decreased. Concurrently, visit rates to emergency departments and hospitals increased (16.1 to 19.7, P trend < 0.001 and 5.6 to 6.0, P trend = 0.01, per 1000 population, respectively). Increases in acute care service use were greatest for anxiety and addictions. Conclusions: The increasing acute care service use coupled with the reduction in outpatient visits suggest, overall, an increase in demand for mental health care that is not being met in ambulatory care settings.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".