Post-traumatic stress disorder and its associated factors among people who experienced traumatic events in east African countries, 2020: a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Post-traumatic stress disorder (PTSD) is the most commonly reported mental health consequence following disasters and traumatic events, either natural or man-made. Nothing is written regarding its pooled prevalence and pooled estimate of factors. Therefore, this study aimed to determine the pooled prevalence of PTSD and estimate the pooled effect of associated factors.An English version of published articles will be retrieved using the following; PubMed/Medline, Africa-wides, Science Direct, Cochrane Library, Global Health, Google Scholar, EMBASE, and psycINFO. Research reports will be searched from October 10/2020 to November 10/2020. The research reports quality will be assessed using the Newcastle-Ottawa Scale. Relevant information from the searched research reports will be extracted in a Microsoft Excel format. After extraction, the data will be imported to STATA version 14.0 for analysis. An appropriate guideline for a systematic review and meta-analysis report will be used, i.e. the Preferred Reporting Items for Systematic reviews and Meta-Analyses. A random-effects meta-analysis model will be used to estimate the Der Simonian and Laird's pooled prevalence of PTSD and its associated factors.This study aims to determine the pooled prevalence of PTSD and estimate the pooled effect of associated factors. Several kinds of research have reported the increasing magnitude of PTSD and its determinants in a different population. This might be due to reasons, such as little attention being given to the issue. Therefore, this study will try to fill this gap by giving new evidence-based results to attract policymakers' attention.
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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 | 0.061 | 0.087 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 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".