Recommendations for Academic Health Libraries Outreach and Engagement Programs with Indigenous Peoples at Collaboration and Empowerment Levels: Striving for Empowerment
Bibliographic record
Abstract
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 The research team also searched specific journals, conference proceedings, and websites, to find unpublished materials and grey literature; they used mailing lists and personal contacts to find further sources.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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".