Canadian Health Libraries’ Responses to the Truth and Reconciliation Commission’s Calls to Action: A Literature Review and Content Analysis
Bibliographic record
Abstract
Abstract Introduction: As part of the Truth and Reconciliation Commission of Canada’s (TRC) Final Report on the history and legacy of residential schools in Canada, ninety-four (94) Calls to Action were identified. Of those, seven are health-specific. The objective of this research paper is to determine how Canadian health library websites are responding to these calls to action. Methods: The authors conducted an initial literature review to gain an understanding of the context of Indigenous health in Canada. A content analysis of Canadian health library websites was conducted to track mentions of the TRC and their responses to the need for Indigenous-focused resources. Results: The results of content analysis indicated few online responses to the TRC’s Calls to Action from Canadian health libraries. Only thirty-three per cent of Canadian health libraries had content that was Indigenous-focused, and only about fifteen per cent of health libraries had visible content related to the TRC’s Calls to Action. Academic and consumer health libraries were more likely to have both TRC- and Indigenous-focused content. Discussion: Nuances related to the research question resulted in some challenges to research design. For example, website content analysis is an imperfect indicator of real-world action. Limitations in research design notwithstanding, visibility is an important part of conveying commitment to the TRC, and the information available indicates the Canadian medical community is not living up to that commitment. Conclusion: Canadian health libraries need to do more to show a visible commitment to the TRC’s Calls to Action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.066 | 0.091 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".