Instruments to identify risk factors associated with adverse childhood experiences for vulnerable children in primary care in low- and middle-income countries: A systematic review and narrative synthesis
Bibliographic record
Abstract
Vulnerable children exposed to Adverse Childhood Experiences (ACEs) are lacking visibility in healthcare and social welfare support systems, particularly in countries where there are delays in integrating biopsychosocial care into traditional medical care. This review seeks to identify, evaluate, and summarise existing screening instruments used in measuring risks factors related to Adverse Childhood Experiences (ACEs) in vulnerable children in Primary Health Care (PHC) settings in low- and middle-income countries (LMICs). The target population in this research is children from age (05-18 years) living in poverty and extreme social disadvantage. First, a systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. A mixed-methods narrative synthesis analyzed the studies and instruments used to assess vulnerable children exposed to ACEs. Each instrument was scrutinized for quality, validity, and feasibility for use with vulnerable children in frontline clinical settings. There is a lack of suitable risk assessment instruments to identify biopsychosocial risk factors from exposure to ACEs in vulnerable children in LMIC primary healthcare settings. Among nine identified instruments from the reviewed studies, none were found suitable for rapidly identifying the effects of ACEs. This was due to issues on the reviewed instruments which could hinder their application in the rapid screening of ACEs in frontline clinical settings. This included the, retrospective nature of the instruments, decisional capacity of the rater, institutional capacity in implementation of the instruments and instruments capacity to assess individual risk factors in biopsychosocial dimensions. Therefore, currently, there is lack of instruments that can be used to identify biopsychosocial risk factors of ACEs in vulnerable children in primary care in limited-resource settings. Further development of an instrument for the rapid identification of ACEs in vulnerable children is required for an early recognition and referred for preventive care, treatment, and social support services.
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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 | 0.025 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".