Screening for adverse social conditions in child healthcare settings: protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Adverse social conditions affect children's development and health outcomes from preconception throughout their life course. Early identification of adverse conditions is essential for early support of children and their families. Healthcare contacts with children provide a unique opportunity to screen for adverse social conditions and to take preventive action to identify and address emerging, potentially harmful or accumulating social problems. The aim of our study is to identify and describe available screening tools in outpatient and inpatient healthcare settings that capture social conditions that may affect children's development, health or well-being. METHODS AND ANALYSIS: We will conduct a systematic review and will report the results following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance. A systematic search of three databases (PubMed (Ovid), PsycInfo (EBSCOhost) and Web of Science Core Collection (Clarivate)) for English-language and German-language articles from 2014 to date will be conducted. We will include peer-reviewed articles that develop, describe, test or use an instrument to screen children for multiple social conditions in paediatric clinics or other outpatient or inpatient child healthcare settings. Key study characteristics and information on screening tools will be extracted and presented in structured tables to summarise the available evidence. We will assess the methodological quality of the instruments with the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist. ETHICS AND DISSEMINATION: Ethical approval is not required for this study as we will not be collecting any personal data. Dissemination will consist of publications, presentations, and other knowledge translation activities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.089 | 0.099 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.095 | 0.012 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".