Psychosocial interventions and mental health in patients with cardiovascular diseases living in low and middle-income countries: A systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to evaluate the effectiveness of psychosocial interventions on mental health outcomes in adult patients with Cardiovascular Diseases (CVDs) living in low- and middle-income countries (LMICs). INTRODUCTION: Mental health issues are highly prevalent among patients with CVDs leading to poor disease prognosis, self-care/ management, and Quality of Life (QOL). In the context of LMICs, where the disease burden and treatment gap are high and resources are inadequate for accessing essential care, effective psychosocial interventions can make significant contributions for improving mental health and reducing mental health problems among patients who live with cardiovascular diseases. INCLUSION CRITERIA: This review will include studies published between 2010 and 2021 that evaluated the effect of psychosocial interventions on mental health outcomes (resilience, self-efficacy, QOL, depression and anxiety) on adult patients (aged ≥18 years) with any cardiovascular diseases using experimental and quasi experimental designs. METHODS: The search will be conducted from the following databases: MEDLINE via OVID (1946-Present), EMBASE via OVID (1974 -Present), Cumulative Index for Nursing and Allied Health Literature (CINAHL) via EBSCOhost (1936-Present), PsycINFO via OVID (1806-Present), Scopus via Elsevier (1976-Present), and Cochrane Library via Wiley (1992-Present). Data will be critically appraised using standard tools and extracted by two reviewers and disagreement will be solved by the third reviewer. Meta-analysis will be performed, if possible, otherwise, data will be synthesized in narrative and tabular forms. DISCUSSION: The findings of this review will provide a key insight into contextually relevant psychosocial interventions for promoting mental health of patients with CVDs living in LMICs. The review findings will be potentially useful for health care providers and researchers to implement such interventions not only for reducing the burden of mental health issues but also for improving the overall well-being among patients with chronic illnesses. SYSTEMATIC REVIEW REGISTRATION NUMBER: Prospero-CRD42020200773.
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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 | 0.051 | 0.055 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.015 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".