Characteristics of Indigenous healing strategies in Canada: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to identify the characteristics of Indigenous healing strategies in Canada and approaches to improving cultural relevance to local Indigenous contexts. INTRODUCTION: In the previous 150 years, Indigenous peoples of Canada have experienced colonization, forced assimilation, cultural oppression and violence, and these are associated with high rates of social distress and health disparities. Today, legacies of colonization continue to marginalize Indigenous peoples, creating healthcare institutions devoid of Indigenous worldviews. Despite the growing number of Indigenous healing strategies currently in existence, literature describing these strategies has not been systematically scoped. To address this gap, this scoping review will identify characteristics of Indigenous healing strategies in Canada, and explore culturally relevant approaches used in research process. INCLUSION CRITERIA: This review will consider literature that describes Indigenous healing strategies in Canada and will include First Nations, Inuit and Métis as the population of interest. Strategies may include, but are not limited to, health services and programs, policies and guidelines, models and frameworks, and Indigenous narratives and expert opinions. Healing strategies delivered in all service settings are eligible for inclusion. METHODS: The databases/sources to be searched will include: CINAHL, Sociological Abstracts, PsycINFO, MEDLINE and Academic Search Premier. Searches for gray literature will be conducted in iPortal, Canadian Electronic Library, and a list of Canadian government and Indigenous organization websites. Retrieval of full-text studies and data extraction will be performed independently by two reviewers. Findings will be summarized in tabular forms accompanied by narrative text.
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 | 0.120 | 0.094 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.034 | 0.035 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".