Impact of the first COVID-19 outbreak on mental health service utilisation at a Dutch mental health centre: retrospective observational study
Bibliographic record
Abstract
BACKGROUND: Previous studies into mental health service utilisation during the COVID-19 pandemic are limited to a few countries or specific type of service. In addition, data on changes in telepsychiatry are currently lacking. AIMS: We aimed to investigate whether the COVID-19 pandemic is associated with changes in mental health service utilisation, including telepsychiatry, and how these changes were distributed among patients with mental illness during the first COVID-19 outbreak. METHOD: This retrospective study obtained routinely assessed healthcare data from a large Dutch mental healthcare institute. Data from the second quarter of 2020 (the first COVID-19 outbreak period) were compared with the pre-pandemic period between January 2018 and March 2020. Time-series analyses were performed with the quasi-Poisson generalised linear model, to examine the effect of the COVID-19 lockdown and the overall trend of mental health service utilisation per communication modality and diagnostic category. RESULTS: We analysed 204 808 care contacts of 28 038 patients. The overall number of care contacts in the second quarter of 2020 remained the same as in the previous 2 years, because the number of video consultations significantly increased (B = 2.17, P = 0.488 × 10-3) as the number of face-to-face out-patient contacts significantly decreased (B = -0.98, P = 0.011). This was true for all different diagnostic categories, although this change was less pronounced in patients with psychotic disorders. CONCLUSIONS: Diminished face-to-face out-patient contacts were well-compensated by the substantial increase of video consultations during the first COVID-19 outbreak in The Netherlands. This increase was less pronounced for psychotic disorders. Further research should elucidate the need for disorder-specific digital mental healthcare delivery.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".