“All About Us”: Indigenous Data Analysis Workshop—Capacity Building in the Canadian Alliance for Healthy Hearts and Minds First Nations Cohort
Bibliographic record
Abstract
BACKGROUND: Research collaborations between non-Indigenous and Indigenous researchers primarily have been led by non-Indigenous researchers with privileged locations in university settings. Recognition of the importance of data sovereignty and control to enable Indigenous self-determination requires building data management and analysis capacities among Indigenous research partners. The Canadian Alliance for Healthy Hearts and Minds First Nations (CAHHM-FN) cohort study, a collaboration of 8 First Nations and researchers at 8 universities, convened a 3-day data management and analysis workshop. METHODS: Before the workshop, participating communities were asked to develop research questions of interest regarding data collected as part of CAHHM-FN and forward them to the coordinating team. An agenda was created, circulated, and modified on the basis of community feedback to plan the workshop. The CAHHM coordinating team, an Indigenous researcher, and a non-Indigenous biostatistician planned the workshop to strike balance among Indigenous protocols for engagement, theory concerning Indigenous approaches to statistical analysis, and applied data analysis training. RESULTS: Fifty participants and coordinating team members convened for the 3-day workshop (22 Indigenous and 28 non-Indigenous people from communities, professors, trainees, and staff). Topics included statistical literacy, hands-on data analysis, data security, and topics in Indigenous health research. Workshop evaluations indicated a high level of satisfaction and enthusiasm to hold similar future workshops. CONCLUSIONS: The Indigenous data workshop was designed to increase capacity for data management and analysis by Indigenous community partners and develop new capacity for non-Indigenous partners and trainees. It achieved this, with enthusiasm from Indigenous community members to conduct future workshops.
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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 | 0.115 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.011 | 0.025 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".