Recommendations for Academic Health Libraries Outreach and Engagement Programs with Indigenous Peoples at Collaboration and Empowerment Levels: Striving for Empowerment
Notice bibliographique
Résumé
A Review of: Cruise, A., Ellsworth-Kopkowski, A., Villezcas, A. N., Eldredge, J., & Rethlefsen, M. L. (2023). Academic health sciences libraries’ outreach and engagement with North American Indigenous communities: A scoping review. Journal of the Medical Library Association, 111(3), 630–656. https://doi.org/10.5195/jmla.2023.1616 Objective – To identify trends and themes in literature sources on interventions for engagement and outreach by academic health sciences libraries with Native Americans, Alaska Natives, First Nations, and Indigenous peoples in the United States, Canada, and Mexico, in order to identify and share effective practices. Design – Scoping review. Setting – Academic health sciences libraries in the United States, Canada, and Mexico. Subjects – Sixty-five reports of 45 engagement and outreach programs spanning 1982-2022. Methods – Researchers conducted a scoping review guided by Arksey and O’Malley’s framework (2005) and the JBI Manual for Evidence Synthesis. They first established inclusion and exclusion criteria then developed a search strategy and ran it across seven bibliographic databases and a library and information science repository. The research team also searched specific journals, conference proceedings, and websites, to find unpublished materials and grey literature; they used mailing lists and personal contacts to find further sources. The researchers used Covidence to screen sources from the bibliographic databases, with English language sources screened by two reviewers and non-English language sources screened by at least one reviewer who could read that language. Sources found via other search methods were screened using Google Sheets, which was also used for data extraction. The researchers analyzed the data using the International Association for Public Participation (IAP2) Spectrum of Public Participation, summarizing programs within the two highest levels to synthesize effective practice. Main Results – The authors identified 45 programs with 27 types of interventions. Training was the most common intervention at 25.5%. They identified 130 different partners; government organizations, both federal and tribal, were the most common at 23.1%. Using the IAP2 Spectrum of Public Participation, a tool designed to assess the level of participation and role of the public in public participation processes, the research team found that five programes (11.1%) engaged with the community at the two highest and also most effective and meaningful levels of collaborate and empower. From these five programs the researchers identified the following areas of effective practice: 1) partnership building and building trust with tribal communities including respecting the knowledge and expertise of the community partners, 2) prioritising and understanding the needs of the tribal communities including developing awareness of cultural differences, 3) partnering with multiple organisations to increase infrastructure, resources, and funding, and, where possible, 4) building on preexisting partnerships and relationships. Conclusion – The authors concluded that libraries are likely to struggle to reach the two highest levels of the IAP2 Spectrum of Public Participation, due to issues with infrastructure, resources, long-term funding, and previous troubled relationships between governments, organizations, and researchers with Native and Indigenous populations, leading to challenges with building and sustaining partnerships. They recommend that libraries initiate any engagement and outreach programs with a needs assessment, with the goal of involving the community partners as collaborators or empowering them as owners and decision makers. The researchers also recommend engaging programs with data sovereignty to increase IAP2 levels and give communities control over their own data.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,049 | 0,110 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,007 |
| Bibliométrie | 0,010 | 0,011 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,011 | 0,017 |
| Science ouverte | 0,006 | 0,008 |
| Intégrité de la recherche | 0,011 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,006 |
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 source (Gemma direct ou Codex distillé), 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 ».