Transdiagnostic Risk and Protective Factors for Psychopathology in Young People: Systematic Review Protocol
Bibliographic record
Abstract
BACKGROUND: Mental and substance use disorders are among the leading causes of burden of disease worldwide, with risk of onset peaking between the ages of 13 and 24 years. Comorbidity is also common among young people and complicates research, diagnosis and assessment, and clinical decision making. There is increasing support for empirically derived models of psychopathology that overcome issues of comorbidity and provide a transdiagnostic framework for investigating the specificity and generality of risk and protective factors for psychopathology. OBJECTIVE: This systematic review aims to identify transdiagnostic risk and protective factors for psychopathology in young people by synthesizing and evaluating findings from research investigating empirically based models of psychopathology. METHODS: Searches will be conducted in Medline, EMBASE, and PsycINFO databases. Reference lists of selected articles will also be hand searched for other relevant publications. All studies will be screened against eligibility criteria designed to identify studies that examined empirical models of psychopathology in relation to risk and/or protective factors in young people with a mean age between 10 and 24 years. Study quality will be assessed using the Joanna Briggs Institute Critical Appraisal Checklists for Cohort Studies and Analytical Cross-Sectional Studies. Findings will be summarized in a narrative synthesis, and a meta-analysis will be conducted if sufficient data are available. RESULTS: This review is ongoing. At the time of submission, full-text screening was completed, and hand searching of selected articles was underway. Results are expected to be completed by the end of 2020. CONCLUSIONS: This protocol is for a systematic review of evidence for transdiagnostic risk and protective factors associated with empirically based models of psychopathology in young people. To our knowledge, the critical synthesis of this evidence will be the first to date and will provide a better understanding of the factors that contribute to the onset and maintenance of psychopathology in young people. Insights drawn from the review will provide critical new knowledge to improve the targeting of interventions to prevent or reduce mental health problems. TRIAL REGISTRATION: This systematic review is registered with PROSPERO (CRD42020161368) and is available via Open Science Framework. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19779.
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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 | 0.077 | 0.085 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.100 | 0.014 |
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".