COVID-19-related consultation-liaison (CL) mental health services in general hospitals: A perspective from Europe and beyond
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic posed new challenges for integrated health care worldwide. Our study aimed to describe newly implemented structures and procedures of psychosocial consultation and liaison (CL) services in Europe and beyond, and to highlight emerging needs for co-operation. METHODS: Cross-sectional online survey from June to October 2021, using a self-developed 25-item questionnaire in four language versions (English, French, Italian, German). Dissemination was via national professional societies, working groups, and heads of CL services. RESULTS: Of the participating 259 CL services from Europe, Iran, and parts of Canada, 222 reported COVID-19 related psychosocial care (COVID-psyCare) in their hospital. Among these, 86.5% indicated that specific COVID-psyCare co-operation structures had been established. 50.8% provided specific COVID-psyCare for patients, 38.2% for relatives, and 77.0% for staff. Over half of the time resources were invested for patients. About a quarter of the time was used for staff, and these interventions, typically associated with the liaison function of CL services, were reported as most useful. Concerning emerging needs, 58.1% of the CL services providing COVID-psyCare expressed wishes for mutual information exchange and support, and 64.0% suggested specific changes or improvements that they considered essential for the future. CONCLUSION: Over 80% of participating CL services established specific structures to provide COVID-psyCare for patients, their relatives, or staff. Mostly, resources were committed to patient care and specific interventions were largely implemented for staff support. Future development of COVID-psyCare warrants intensified intra- and inter-institutional exchange and co-operation.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".