Impact of the first COVID-19 outbreak on mental health service utilisation at a Dutch mental health centre: retrospective observational study
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it