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Record W4229812640 · doi:10.1353/cpr.2014.0048

Community-Based Research in Action: Tales From the Ktunaxa Community Learning Centres Project

2014· article· en· W4229812640 on OpenAlexaboutno aff
Elizabeth A. Stacy, Katherine Wisener, Yolanda Liman, Olga Beznosova, Helen Novak Lauscher, Kendall Ho, Sandra Jarvis-Selinger

Bibliographic record

VenueProgress in community health partnerships · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsAction researchSociologyPublic relationsParticipatory action researchNegotiationCommunity healthCommunity engagementPedagogyNursingPolitical scienceMedicinePublic healthSocial science

Abstract

fetched live from OpenAlex

Community-Based Research in Action:Tales From the Ktunaxa Community Learning Centres Project Elizabeth Stacy, MA, Katherine Wisener, MA, Yolanda Liman, MA, Olga Beznosova, PhD, Helen Novak Lauscher, PhD, Kendall Ho, MD, FRCPC, and Sandra Jarvis-Selinger, PhD What Is the Purpose of This Study? • To chronicle and evaluate the community-based research process used in the development of the Community Learning Centres (CLCs) in rural Canadian communities, specifically Aboriginal communities. In particular, this study considers culturally and geographically relevant health information while focusing on the process of building relationships among the partners as well as the value and sustainability of the CLC model. What Is the Problem? • Many rural Canadian communities, especially Aboriginal communities, have inadequate access to geographical or socially relevant health information, which has detrimental impacts on community members’ health and well-being. • Little research exists on implementing and evaluating CLCs in Aboriginal communities as a way of addressing the lack of culturally and geographically suitable health and social service information. What Are the Findings? • The analysis of interviews revealed four themes: People with different cultural backgrounds finding ways to work together, project employees taking ownership over and a lead in the project’s success, community leads who coordinated CLCs gaining technical and research skills, and the university team learning about collaborative research in Aboriginal communities. • Participants identified several process challenges, including working out how to run the project cooperatively, negotiating different working styles, and hiring people who were committed to the success of the project. • Having a CLC in the community made it possible for community members to learn about health information and further the progress of other community initiatives. Who Should Care Most? • Health care practitioners, including nursing, medical, and allied health and social service professionals. • Rural community development officers and/or researchers. • Aboriginal health care programs. • Aboriginal and Canadian Governments. [End Page 261] Recommendations for Action • Meet all partners “where they are” in building trusting relationships and adapting research methods to fit the context and strengths of the project. • Include a joint university–community training session or facilitated discussion to become familiar with both university and community protocols to initiate the process of comparing and contrasting community and post-secondary procedures. In addition, create strategies for working through differences, break project into small goals.. • Consider how technological access provided by the CLCs will increase capacity for learning and collective initiatives in the community, as well as the skills, knowledge, and perceived self-efficacy of the community research leads. As such, plan for additional support services, such as information technology training, as capacity and confidence increases in communities. • Incorporate a hiring rubric matrix where the emphasis is placed on commitment to the project objectives to effectively meet different working styles and levels. • Plan for turnover in community research staff by including a work plan for alternative research members to maintain responsibility for community-based features of the project. [End Page 262] Elizabeth Stacy, Katherine Wisener, Yolanda Liman, Olga Beznosova, Helen Novak Lauscher, Kendall Ho, and Sandra Jarvis-Selinger University of British Columbia Copyright © 2014 The Johns Hopkins University Press

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)0.072
metaresearch head score (Gemma)0.044
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: none
Teacher disagreement score0.928
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0720.044
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0450.031
Scholarly communication0.0230.007
Open science0.0070.031
Research integrity0.0090.024
Insufficient payload (model declined to judge)0.0070.002

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 head0.803
GPT teacher head0.634
Teacher spread0.169 · 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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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