Adverse childhood experiences, morbidity, mortality and resilience in socially excluded populations: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Socially excluded populations, defined by homelessness, substance use disorder, sex work or criminal justice system contact, experience profound health inequity compared with the general population. Cumulative exposure to adverse childhood experiences (ACEs), including neglect, abuse and household dysfunction before age 18, has been found to be independently associated with both an increased risk of social exclusion and adverse health and mortality outcomes in adulthood.Despite this, the impact of ACEs on health and mortality within socially excluded populations is poorly understood. METHODS AND ANALYSIS: We will search MEDLINE, Cumulative Index of Nursing and Allied Health Literature, Educational Resources Information Center, PsycINFO, Applied Social Science Index and Abstracts and Criminal Justice Database for peer-reviewed studies measuring ACEs and their impact on health and mortality in socially excluded populations.Three review questions will guide our data extraction and analysis. First, what is the prevalence of ACEs among people experiencing social exclusion in included studies? Second, what is the relationship between ACEs and health and mortality outcomes among people experiencing social exclusion? Does resilience modify the strength of association between ACEs and health outcomes among people experiencing social exclusion?We will meta-analyse the relationship between ACE exposure and health outcomes classified into six a prior categories: (1) substance use disorders; (2) sexual and reproductive health; (3) communicable diseases; (4) mental illness; (5) non-communicable diseases and (6) violence victimisation, perpetration and injury. If there are insufficient studies for meta-analysis, we will conduct a narrative synthesis. Study quality will be assessed using the MethodologicAl STandards for Epidemiological Research scale. ETHICS AND DISSEMINATION: Our findings will be disseminated in a peer-reviewed journal, in presentations at academic conferences and in a brief report for policy makers and service providers. We do not require ethics approval as this review will use data that have been previously published. PROSPERO REGISTRATION NUMBER: CRD42022357565.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.059 | 0.089 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.032 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.004 |
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".