Evidence Of Validity And Reliability Of Instruments Available For Clinical Prioritization In Outpatient Mental Health In Chile: A Scoping Review
Bibliographic record
Abstract
Mental health disorders represent a leading cause of disability worldwide, with Chile exhibiting one of the highest burdens in Latin America. Long waiting times—often exceeding nine months—for psychiatric care highlight the urgent need for prioritization strategies in ambulatory services. This study aimed to identify instruments available for clinical prioritization in mental health and assess their psychometric properties to guide evidence-based adaptation in Chile. International evidence underscores the value of structured triage and risk stratification tools to enhance timely access and optimize scarce psychiatric resources. However, most studies focus on emergency settings, with limited data for community or ambulatory contexts. This knowledge gap creates a barrier to effective implementation in Chile’s community mental health centers (COSAM), where demand consistently outpaces capacity. A scoping review was conducted using PubMed, CINAHL, and BVS databases (2016–2025). Inclusion criteria targeted adult ambulatory populations, instruments for triage or prioritization, and reported psychometric data. The JBI methodological framework and the acronym PCC and PRISMA guidelines were used. Methodological quality was assessed with the Newcastle-Ottawa Scale. 436 studies were retrieved, of which three articles were selected. The UK Mental Health Triage Scale demonstrated excellent interrater reliability (ICC=0.997). The Intensive Case Management Screening Sheet showed adequate internal consistency (α=0.77) and predictive validity for service needs. A combined psychosocial screener (PHQ-4, CAGE, PC-PTSD) was feasible but lacked consolidated psychometric evaluation. Collectively, these tools support improved decision-making, efficiency in specialist allocation, and potential reduction in wait times, before a possible cultural adaptation. This APN-led research underscores the innovation of adapting structured prioritization tools to ambulatory and community mental health contexts in Latin America. Future research should focus on local validation, feasibility studies, and system-level evaluation of impact on waiting lists, risk reduction, and equity of care. Integrating robust psychometrics with practical implementation strategies may strengthen advanced practice nursing leadership in optimizing mental health service delivery. Advanced practice nurses are uniquely positioned to lead this evidence-based transformation, bridging research, clinical care, and health system impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".