MétaCan
Menu
Back to cohort
Record W4408339803 · doi:10.1177/10775595251322084

The Role of Protective Adults in Mitigating Health Outcomes Linked to Childhood Physical and Sexual Abuse

2025· article· en· W4408339803 on OpenAlexaff
Shannon Halls, Philip Baiden, Andie MacNeil, Esme Fuller‐Thomson

Bibliographic record

VenueChild Maltreatment · 2025
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSexual abuseMental healthMedicinePhysical abuseBehavioral Risk Factor Surveillance SystemOddsPoison controlChild abuseProtective factorOccupational safety and healthInjury preventionSuicide preventionLogistic regressionPsychological abuseYoung adultOdds ratioPsychiatryClinical psychologyEnvironmental healthGerontologyPopulation

Abstract

fetched live from OpenAlex

Childhood physical and/or sexual abuse are associated with negative physical and mental health outcomes in adulthood. Protective factors may contribute to resilience and reduce the risk of these adult health outcomes. This study aims to determine if the presence of a protective adult can mitigate the association between childhood abuse and negative adult health outcomes. Data were obtained from the 2021 and 2022 Behavioral Risk Factor Surveillance System ( n = 83,495). Binary logistic regression was used to compare the odds of health outcomes in adults who experienced abuse before age 18 compared to those who did not, adjusting for the presence of a protective adult and socio-demographic, socioeconomic, and health behavior factors. Childhood physical and/or sexual abuse were associated with higher odds of physical and mental health conditions in adulthood. Adjusting for the presence of a protective adult partially attenuated the odds of many adult health outcomes. Understanding protective factors associated with childhood abuse may improve targeted outreach and provide helpful direction for the development of effective programs for children experiencing abuse.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.290
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueChild MaltreatmentSame topicChild Abuse and TraumaFrench-language works237,207