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“Do it for Your Kid”: Resilience and Mothering in the Context of Intimate Partner Violence in Rural Ontario

2024· article· en· W4400060859 on OpenAlexaffabout
Kimberley T. Jackson, Panagiota Tryphonopolous, Julia Yates, Katie J. Shillington, Tara Mantler

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsContext (archaeology)Domestic violenceResilience (materials science)SociologyPsychologyCriminologyGender studiesDevelopmental psychologyGeographySuicide preventionPoison controlEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Intimate partner violence (IPV) includes multiple forms of harm inflicted on an intimate partner. Experiences of IPV impact mental and physical health, social relationships, and parenting and resilience may play an important role in how women overcome these detrimental effects. There is little research on how resilience relates to mothers' experience of IPV. We explored the role of resilience in the context of mothers who have experienced IPV in rural settings via semi-structured interviews with six women and 12 service providers. The relationship between resilience and motherhood was a common theme across all narratives. From this theme emerged three subthemes: 1) breaking the cycle of abuse; 2) giving children the "best life"; and 3) to stay or to leave: deciding "for the kids". Findings underscore the importance of supporting rural women who experience violence in cultivating their resilience and consideration of policy changes which support trauma- and violence-informed care.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.480

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0180.006
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.316
Teacher spread0.271 · 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 designQualitative
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

Citations2
Published2024
Admission routes2
Has abstractyes

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