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Record W7133005747

Inclusive Accessible or Culturally Relevant? Two Perspectives on Accounting for Culture in Community-based Stroke Physical Activity Programs

2023· dissertation· W7133005747 on OpenAlexaboutno aff
Joseph Fulton

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupMindsetCultural diversityCultural competenceCulturally appropriateCulturally sensitiveDiversity (politics)Ethnically diverse
DOInot available

Abstract

fetched live from OpenAlex

With the growing prevalence of stroke, recognition of health disparities experienced by ethnically marginalized communities, and growing cultural diversity across Canada, community-based organizations have a distinct opportunity to address the unique barriers and cultural needs of culturally diverse individuals with stroke. This thesis explores how eight program leaders within Canadian community-based organizations account for ethnicity and culture in the development of stroke physical activity programs. Three themes were identified based on interviews with program leaders: (1) multiple definitions of culturally relevant programs, (2) inclusive & accessible and culturally relevant program attributes, and (3) inclusive & accessible mindset versus culturally relevant mindset. The findings of this thesis highlight the variability of program leaders’ perspectives when defining culturally relevant and their approaches to designing and delivering programs for ethnically and culturally diverse people with stroke, including whether or how they integrate cultural needs into programs, which they describe is often a decision made by program leaders rather than a requirement of programs or organizations.

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)0.019
metaresearch head score (Gemma)0.025
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 score0.146
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0200.031
Scholarly communication0.0140.009
Open science0.0020.011
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0020.000

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 head0.533
Teacher spread0.413 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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