Protocol for a systematic review of interventions targeting mental health, cognition or psychological well-being among individuals with long COVID
Bibliographic record
Abstract
INTRODUCTION: For some people, COVID-19 infection leads to negative health impacts that can last into the medium or long term. The long-term sequelae of COVID-19 infection, or 'long COVID', negatively affects not only physical health, but also mental health, cognition or psychological well-being. Complex, integrated interventions are recommended for long COVID, including psychological components; however, the effectiveness of such interventions has yet to be critically evaluated. This protocol describes a systematic review to be conducted of scientific literature reporting on clinical trials of interventions to promote mental health, cognition or psychological well-being among individuals with long COVID. METHODS AND ANALYSIS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines will be followed. A health sciences librarian will identify the relevant literature through comprehensive systematic searches of Medline, Embase, APA PsycINFO, Cumulative Index to Nursing and Allied Health Literature, medRxiv, PsyArXiv, China National Knowledge Internet and WANFANG Data databases, as well as The Cochrane Central Register of Controlled Trials, clinicaltrials.gov and the WHO International Clinical Trials Registry Platform. Studies will be selected through a title and abstract review, followed by a full-text review using inclusion and exclusion criteria. Data extracted will include intervention descriptions and efficacy metrics. Data will be narratively synthesised; if the data allow, a meta-analysis will be conducted. Risk of bias assessment will be conducted using the Cochrane Risk of Bias 2.0 tool. ETHICS AND DISSEMINATION: Ethical approval for systematic reviews is not required. As researchers and clinicians respond to the new clinical entity that long COVID represents, this review will synthesise a rapidly emerging evidence base describing and testing interventions to promote mental health, cognition or psychological well-being. Results will therefore be disseminated through an open-access peer-reviewed publication and conference presentations to inform research and clinical practice. PROSPERO REGISTRATION NUMBER: CRD42022318678.
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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 | 0.093 | 0.144 |
| Meta-epidemiology (narrow) | 0.009 | 0.009 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.153 | 0.026 |
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".