Mapping the landscape of mental health and long COVID: a protocol for scoping review
Bibliographic record
Abstract
INTRODUCTION: Mental health concerns are prevalent among adult patients with long COVID (LC), but the current state of knowledge regarding mental health in the context of LC is not fully understood. The objective of this scoping review is to map and summarise the existing research on mental health conditions among LC patients and highlight the knowledge gaps. This review aims to provide a comprehensive overview of the evolving landscape of research in the area. METHODS AND ANALYSIS: The concept of interest is mental health in adult LC patients. This scoping review will be guided by the Joanna Briggs Institute Manual for Evidence Synthesis and reported according to the recommendations in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review guidelines. Using predefined search parameters, a comprehensive search of two electronic databases (Medline and APA PsycINFO) and grey literature sources identified 3104 potentially eligible articles published from 1 January 2020 to 4 April 2024. Following the removal of duplicates, 2767 articles were imported for screening in Covidence. The study selection process involves screening titles, abstracts and full text of potentially relevant articles, which will then be analysed using thematic analysis. Data will be extracted using a predefined extraction form. ETHICS AND DISSEMINATION: Ethical approval is not required because this study does not involve human participants or primary data collection. The findings from this review will be disseminated through a peer-reviewed publication, conference presentations and professional networks. In addition, a summary of the results will be shared with patient partners and other relevant stakeholders. PUBLIC HEALTH IMPLICATIONS: The findings from this scoping review will contribute to a better understanding of mental health issues arising in LC patients and inform future research directions and public health interventions in this area.
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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 | 0.176 | 0.154 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 0.018 |
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".