A clash of paradigms? Western and indigenous views on health research involving Aboriginal peoples
Bibliographic record
Abstract
AIM: To explore the issues of data management and data ownership with regard to health research conducted in aboriginal or indigenous populations in Canada. BACKGROUND: Research with aboriginal communities in Canada has often been conducted by researchers who had little or no understanding of the community in which the research was taking place. This led to 'helicopter' research, which benefitted the researcher but not the community. National aboriginal leadership developed the ownership, control, access, and possession (OCAP) principles, which outline how to manage research data regarding aboriginal people and to counteract disrespectful methodologies. However, these principles present their own set of challenges to those who would conduct research with aboriginal populations. DATA SOURCES: Documents from the Assembly of First Nations, the Government of Canada, Aboriginal writers and researchers, and Nursing theorists and researchers. REVIEW METHODS: This is a methodology paper that reviews the issues of data ownership when conducting research with Aboriginal populations. DISCUSSION: The authors explore indigenous and Western views of knowledge development, outline and discuss the OCAP principles, and present the Canadian Institute of Health Research's guidelines for health research involving aboriginal people as a guide for those who want to carry out ethical and culturally competent research, do no harm and produce research that can benefit aboriginal peoples. CONCLUSION: There are special considerations associated with conducting research with Aboriginal populations. The Assembly of First Nations wants researchers to use the Ownership, Control, Access and Possession (OCAP) principles with First Nations data. These principles are restrictive and need to be discussed with stakeholders before research is undertaken. IMPLICATIONS FOR PRACTICE/RESEARCH: In Canada, it is imperative that researchers use the Canadian Institute of Health Research Guidelines for Health Research Involving Aboriginal People to ensure culturally sensitive and ethical conduct during the course of the research with Aboriginal populations. However, some communities may also want to use the OCAP principles and these principles will need to be taken into consideration when designing the study.
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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 | 0.276 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.022 | 0.139 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".