Psychiatric disorders among people with cancer in low- and lower-middle-income countries: study protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Cancer is a rapidly growing public health problem in low- and lower-middle-income countries (LLMICs). There is evidence from upper-income countries that comorbid mental illness is common and can adversely impact cancer outcomes. Little is known about this burden in LLMICs. This systematic review has two aims. The first is to review the prevalence and patterns of psychiatric comorbidity in adults with cancer in LLMICs. The second is to review psychiatric treatment outcomes in this population. METHODS AND ANALYSIS: The review will be reported according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A systematic search of electronic databases (MEDLINE, PsycInfo, Embase and CINAHL) will be conducted. Studies will be included if they report the prevalence of psychiatric comorbidity, or if they evaluate psychiatric treatment outcomes, in adults with cancer living in LLMICs. The search will be limited to studies published in peer-reviewed journals between March 2002 and March 2017. The reference lists of included studies will be hand searched. Critical appraisal will be performed using Quality Assessment Tools from the National Institute of Health. Pooled prevalence meta-analysis is planned. ETHICS AND DISSEMINATION: Ethics approval is not required as no primary data will be collected. The results will be presented at conferences and published in a peer-reviewed journal. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017057103.
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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 | 0.056 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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".