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Record W4404066568 · doi:10.33043/z6c9ayb6

Zooming In and Out of Programming: Developing an Approach to Trauma-and Violence-Informed Physical Activity “Post-Pandemic”

2024· article· en· W4404066568 on OpenAlexaff
Candace Roberts, Francine Darroch, Lyndsay Hayhurst

Bibliographic record

VenueSport Social Work Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsYork UniversityCarleton University
Fundersnot available
KeywordsPandemicZoomCoronavirus disease 2019 (COVID-19)Medical emergencyPsychologyMedicineEngineering

Abstract

fetched live from OpenAlex

Researchers have found that physical activity is an effective health promotion tool due to its positive effect on wellbeing, however, despite the overwhelming evidence on the benefits of physical activity, many structural and systemic inequities exist that affect access and uptake particularly for women. These barriers have been exacerbated over the last few years as conditions surrounding the COVID-19 pandemic has made engagement in physical activity even more difficult for equity-deserving populations. Community organizations have reported an increase in gender-based violence and a strain on support services. In this qualitative research study, we present findings that demonstrated how COVID-19 complicated the delivery of in-person programs for equity-deserving populations. Using a feminist participatory action research approach, community-specific barriers to physical activity from the perspectives of individuals who deliver physical activity programming and social services to self-identified women were generated in three themes using thematic analysis: 1) Increased Need, Decreased Services; 2) Online Service Provision was Not Effective for Clients or Providers; 3) Physical Activity was Not Deemed an “Essential Service”-Transitioning from Survival Mode to a New Normal. Taken together, findings underlined the importance of effective and sustainable resources and strategies to improve access for women to engage in physical activity programs.

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)0.003
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 score0.738
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.440
Teacher spread0.333 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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