Longitudinal insights into psychiatric presentations and service utilization among older adults in Alberta from 2017 to 2022: Effects of the COVID-19 pandemic
Bibliographic record
Abstract
Background: Psychiatric service utilization is influenced by psychological distress, demographic, and contextual factors. This study examined trends in psychiatric presentations among community-dwelling older Albertans (65+) from pre-pandemic to postpandemic phases. Methods: A retrospective longitudinal cohort design analyzed psychiatric presentations using Alberta physician billing data from 2017 to 2022 using ICD-9/10 codes. Rates were compared across pandemic phases. Descriptive statistics, one-way analysis of variance, and general linear model (GLM) evaluated the effects of the pandemic, sex, geographic location, and healthcare setting. Results: There were over 1.68 million psychiatric presentations among older adults during the study period. Overall presentations increased by +24.82% during the pandemic, primarily due to a +26.66% rise in outpatient visits, which stabilized but did not return to pre-pandemic levels. Females comprised 62.82% of encounters, with a nonsignificant +27.75% increase during the pandemic that stabilized. Males showed a +39.95% increase during COVID-19, then a −9.86% decline postpandemic – both nonsignificant. GLM analysis identified geographic location and healthcare setting as significant predictors of psychiatric presentations among older adults; females and urban residents had higher outpatient use. In outpatient settings, urban residence predicted presentations, while rural status approached significance for emergency visits. No significant trends were found for inpatient care. Conclusion: Findings suggest the mental health impact of the pandemic persisted post-pandemic, exacerbating disparities in psychiatric service utilization, highlighting the need for targeted interventions such as telepsychiatry and expanded outpatient services. Future research should focus on interventions aimed at reducing geographic disparities in mental healthcare access.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".