The impact of <scp>COVID</scp>‐19 on the mental health workforce: A rapid review
Bibliographic record
Abstract
The COVID-19 pandemic led to significant adaptations to healthcare. Provision of mental healthcare in a changing environment presented healthcare workers with unique challenges and demands, including changes in workload and expectations. To inform current and future healthcare service responses, and adaptations, the current review aimed to collate and examine the impact of the pandemic on mental healthcare workers (MHWs). We conducted a rapid systematic review to examine the overall impact of the COVID-19 pandemic on MHWs. Searches were conducted in Ovid Medline and PsycInfo and restricted to articles published from 2020. Inclusion criteria specified articles written in English, published in peer-reviewed journals, and that examined any outcome of the impact of COVID-19 on MHWs; 55 articles fulfilled these criteria. Outcomes were categorized into 'work-related outcomes' and 'personal outcomes'. Mental healthcare workers worldwide experienced a range of work-related and personal adversities during the pandemic. Key work-related outcomes included increased workload, changed roles, burnout, decreased job satisfaction, telehealth challenges, difficulties with work-life balance, altered job performance, vicarious trauma and increased workplace violence. Personal outcomes included decreased well-being, increased psychological distress and psychosocial difficulties. These outcomes differed between inpatient, outpatient and remote settings. The COVID-19 pandemic significantly altered the delivery of mental healthcare and MHWs experienced both work-related and personal adversities during the COVID-19 pandemic. With the continuation of changes introduced to healthcare in the initial stages of the pandemic, it will be important to maintain efforts to monitor negative outcomes and ensure supports for MHWs, going forward.
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 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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".