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Record W3036808536 · doi:10.1177/1077801220923748

Promoting Wellness and Recovery of Young Women Experiencing Gender-Based Violence and Homelessness: The Role of Trauma-Informed Health Promotion Interventions

2020· article· en· W3036808536 on OpenAlexafffundabout
Nadine Reid, Amie Kron, Thanara Rajakulendran, Deborah Kahan, Amanda Noble, Vicky Stergiopoulos

Bibliographic record

VenueViolence Against Women · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCovenant HealthUniversity of TorontoCentre for Addiction and Mental Health
FundersPublic Health Agency of Canada
KeywordsPsychoeducationPsychological interventionThematic analysisPopulationSuicide preventionPoison controlPsychologyHealth promotionQualitative researchMedicineOccupational safety and healthCoping (psychology)Clinical psychologyNursingPublic healthEnvironmental healthSociology

Abstract

fetched live from OpenAlex

Little is known regarding the types of interventions most effective in supporting wellness and recovery of victims of gender-based violence, particularly those simultaneously experiencing homelessness. This qualitative study explored the experiences of 18 young women experiencing gender-based violence and homelessness who participated in a community-based, trauma-informed group intervention in Toronto, Canada. Participants completed audio-recorded and transcribed semi-structured interviews, analyzed using thematic content analysis. Participants described valuing the safe, women-only space, shared lived experiences, and tailored psychoeducation and resulting improvements in confidence, coping, health, relationships, and future directedness. Findings suggest community-based, trauma-informed group interventions can facilitate wellness and recovery in this population.

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)0.003
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.053
GPT teacher head0.359
Teacher spread0.306 · 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

Citations35
Published2020
Admission routes3
Has abstractyes

Explore more

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