Community-Based Research in Action: Tales From the Ktunaxa Community Learning Centres Project
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,116 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,022 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,037 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».