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Record W4401769881 · doi:10.1080/23311886.2024.2391533

Adverse childhood experiences, mothers and homelessness: a narrative review and recommendations

2024· review· en· W4401769881 on OpenAlexaff
A. Köhler, Nicole Pylypchuk, Emilene Reisdorfer

Bibliographic record

VenueCogent Social Sciences · 2024
Typereview
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsThe King's UniversityMacEwan University
Fundersnot available
KeywordsAdverse Childhood ExperiencesSexual abuseDomestic violenceNarrativeCycle of violencePsychologyDevelopmental psychologyChildhood abuseNormalization (sociology)Poison controlSuicide preventionPsychiatryMedicineMental healthSociologyEnvironmental health

Abstract

fetched live from OpenAlex

Homelessness is a complex and pervasive worldwide social crisis that profoundly affects a diverse range of individuals and communities. Adverse childhood events (ACEs) are traumatic events that can lead to significant negative effects during adulthood, including homelessness. In women who are mothers, the pathways to loss of housing include, but are not limited to: a history of ACEs, weak social networks, sexual violence, and intimate partner violence. This narrative review of the literature aimed at examining the evidence of adverse childhood experiences and homelessness in adult women who are mothers and to providing recommendations for practice. Across the eight articles included and analyzed, six common themes emerged: family fragmentation, out-of-family placement, abuse, learned substance abuse, a lack of formal and informal education, and normalization and internalization of ACEs. The results showed that children who experience ACEs and become mothers in adulthood might have increased chances of becoming homeless and repeating an intergenerational cycle of trauma onto their children.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.120
GPT teacher head0.501
Teacher spread0.381 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

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