Academic Health Sciences Libraries' Outreach and Engagement with North American Indigenous Communities: A Scoping Review
Bibliographic record
Abstract
Objective: Through this scoping review we sought to identify trends and themes in how academic health sciences libraries in the United States, Canada and Mexico have supported engagement and outreach with Native Americans, Alaska Natives, First Nations, and Indigenous peoples, in or from those same countries. We also sought to learn and share culturally effective practices for libraries engaging with these communities.Methods: We searched seven bibliographic databases, E-LIS, and multiple sources of grey literature. Results were screened using Covidence and Google Sheets. We utilized Arksey and O’Malley’s framework for scoping reviews and followed principles from Joanna Briggs Institute’s Manual for Evidence Synthesis. We also followed the PRISMA and PRISMA-S guidelines. We determined types of interventions used by academic health sciences libraries in engagement with our included populations, the level of public participation reached by these interventions, what partnerships were established, and what practices emerged.Results: Database searching returned 2,050 unique results, 110 of which were relevant and screened in full text. Additional searching returned 211 unique results for full text screening. After screening, 15 results from database searching were included, and 50 from other sources. From these 65 reports, 45 unique programs were identified. Data extraction was conducted on these programs to identify partners, intervention type, and evaluation method. The programs were categorized using the IAP2 Spectrum of Public Participation.Conclusions: Our scoping review found that many programs were health information trainings, and did not move beyond informing the public with little further involvement. The need for sustained funding, greater community participation and more publishing on engagement and outreach are discussed.
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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 | 0.041 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.030 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".