Making space for Indigenous knowledge translation activities in Canadian health librarianship
Bibliographic record
Abstract
Abstract Introduction: In 2015, the Truth and Reconciliation Commission’s Call to Action 22 called upon “those who can effect change within the Canadian health-care system to recognize the value of Aboriginal healing practices” 1. Given that research has shown that health librarians have this ability to help affect change in the healthcare system, how does the health library literature reflect shifting professional practice that make space for Indigenous peoples and knowledge? Methods: In this literature review, we searched for keywords and controlled terms, or subject headings, on Indigenous topics within three key North American health library journals: the Journal of the Canadian Health Libraries Association (JCHLA) (indexed in CINAHL); the Journal of the Medical Library Association (JMLA) (Ovid MEDLINE); and Medical Reference Services Quarterly (MSRQ) (Ovid MEDLINE). A modified version of Smylie et al’s2 model of critical self-reflection guided by Indigenous knowledge translation principles was then used to analyse the papers and suggest potential avenues for future research. Results: Our initial search retrieved 8 articles from JMLA, 2 from MRSQ, and 14 from JCHLA which our exclusion criteria reduced to 5 articles that were then qualitatively analyzed. Of these only two articles reflected a more substantial engagement with Indigenous knowledge translation practices. Discussion: Most of the articles did not explicitly engage in self-reflection about how their personal, professional or systemic privileges and biases impact their work with Indigenous health topics.
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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 | 0.061 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".