Interventions to support mental health in people with long COVID: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Long COVID (LC) is a multisystem disease with symptoms lasting weeks or months beyond the acute COVID-19 infection. Several manifestations are reported by people with LC, including effects on mental health, with varying degrees of psychological distress and disturbances to daily activities. Research conducted to identify effective interventions to support mental health among people with LC has been limited by the breadth and scope of studies. AIM: This review aims to identify interventions being tested to support mental health of people with LC. METHODS: A scoping review was conducted by searching five databases for articles published between January 2020 and early October 2022 to identify research evaluating interventions focused on improving mental health symptoms associated with LC. Results from all sources were checked for eligibility by two reviewers, and agreements were resolved by discussion. Gray literature and reference list of included studies and relevant reviews were scrutinised to identify any additional studies. Data extraction was conducted by one reviewer and checked by another reviewer for accuracy. RESULTS: Of the 940 studies identified, 17 were included, the design of which varied but included mainly case studies (n = 6) and clinical trials (n = 5). Several interventions were described, ranging from single interventions (e.g., pharmacologic) to more holistic, comprehensive suites of services (pharmacologic and non-pharmacologic). Several mental health outcomes were measured, mostly anxiety and depression. All included studies were reported to be associated with improvements in participants' mental health outcomes. CONCLUSION: This scoping review identified studies reporting on a variety of interventions to support mental health among people with LC. Although positive changes were reported by all studies, some were case studies and thus their findings must be interpreted with caution. There is a need for more research to be conducted to identify the impact of interventions on mental health of people with LC.
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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 | 0.019 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".