Adapting mental health services to the COVID-19 pandemic: reflections from professionals in four countries
Bibliographic record
Abstract
The COVID-19 pandemic significantly changed the lives of a majority of the world’s population. People have been encouraged to implement social distancing behaviors enforced by governments, and have experienced loss of employment or changes to their usual working environment. In the mental health sector, psychologists and psychiatrists have been forced to alter the standard care of patients without compromising safety. This article documents the experiences of the authors – mental health professionals in four countries, Canada, Russia, Australia and Japan – at the time of the COVID-19 pandemic, and offers recommendations on how clinical, training, and research practices may need to be adjusted to deal with lockdown situations. Clinicians adapted their usual best practices by learning new skills and updating their knowledge base. Mental health clinicians noticed that the pandemic led to symptomatic changes in some of their patients. Most clinicians moved towards providing telemental health services, such as conducting assessments and treatments remotely. Those who continued seeing patients in person employed personal protective equipment with various impacts on the clinician–patient relationship. The dilemmas of mass quarantines need to be carefully examined, as their effects on numerous health and psychosocial variables appear to be far-reaching.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.009 | 0.017 |
| 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".