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Record W4308773410 · doi:10.1177/10778012221134821

Taking Practical Steps: A Feminist Participatory Approach to Cocreating a Trauma- and Violence-Informed Physical Activity Program for Women

2022· article· en· W4308773410 on OpenAlexafffund
Francine Darroch, Colleen Varcoe, Gabriela Gonzalez Montaner, Jessica M. Webb, Michelle M. Paquette

Bibliographic record

VenueViolence Against Women · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of British ColumbiaCarleton University
FundersMichael Smith Health Research BCBanting Research Foundation
KeywordsParticipatory action researchCitizen journalismDomestic violencePoison controlAction (physics)Human factors and ergonomicsSuicide preventionPsychologyMedicineNursingSocial psychologyMedical emergencySociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Trauma- and violence-informed physical activity (TVIPA) is a feasible approach to improve access/engagement in physical activity for pregnant/parenting women with experiences of trauma. Through feminist participatory action research, 56 semistructured interviews were completed to understand TVIPA. Four themes were identified: (1) "I have to be on edge": Trauma and violence pervade women's lives, (2) "It should be mandatory that you feel safe": Emotional safety is essential, (3) "The opportunity to step up and be decision-makers and leaders": Choice, collaboration, and connection create safety, and (4) "It's a good start for healing," strengths-based and capacity building foster individual and community growth.

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)0.038
metaresearch head score (Gemma)0.028
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 score0.038
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.010
Scholarly communication0.0050.003
Open science0.0030.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.077
GPT teacher head0.405
Teacher spread0.328 · 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

Citations31
Published2022
Admission routes2
Has abstractyes

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