A protocol of a systematic review and meta-analysis on the prevalence of mental health disorders among older adults and synthesis of community-based interventions among them in low- and middle-income countries
Bibliographic record
Abstract
Background: Older adults of low- and middle-income countries (LMICs) have a higher prevalence of mental health disorders compared to high-income countries, which can be attributed to a combination of social factors (such as isolation and loss of spouse), declining physical well-being, and the cumulative impact of life stressors, all of which contribute to an increased vulnerability in mental health. Objectives: The objective of this study was to determine the pooled prevalence of mental health disorders among older adults in LMICs and to identify community-based mental health intervention measures to tackle mental health problems in LMICs. Methods: Articles will be retrieved from databases such as PubMed, Scopus, and Cochrane Central using MeSH terms, keywords, and text words for each concept. Preprint search servers (MedRx, Cogprints, IndiaRxiv, medRxiv, and SSRN) will also be accessed. Data analysis will be conducted as per guidelines provided in the Handbook of Cochrane Systematic Review. RevMan version 5.4 software will be used for pooled analysis. Expected outcome: This systematic review will be able to identify the pooled prevalence of mental health among older adults and will also identify the evidence-based mental health intervention measures in community settings among older adults of LMICs.
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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 | 0.146 | 0.173 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.030 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".