Ready to Practice Indigenous Health Research? An Integrative Framework of Indigenous Health Research Competencies for NEIHR Network Evaluation, Training, and Selection
Bibliographic record
Abstract
Background: National Environments for Indigenous Health Research (NEIHR) are funded across Canadian provinces to enhance capacities for Indigenous health research. While capacities are required at political, organizational, and individual levels, individual capacity is critical to unpack as it is related to capacity at all levels. In the XXXXXXXXXXXX network, our evaluation team has been working to identify the knowledge, skills, abilities, and other characteristics (KSAOs) required for competency and readiness to practice Indigenous health research. Method: Utilizing competency modeling techniques, we brought together XXXXXX members to participate in an idea generation session regarding Indigenous health research competencies. Competencies were grouped using a qualitative cut-and-sort technique (Ryan & Bernard, 2003) and juxtaposed with readiness to practice in healthcare domains from non-Indigenous and Indigenous literatures in a co-occurrence matrix. Results: The idea generation session produced 151 statements about Indigenous health research competencies, from which 42 non-redundant KSAOs were derived. Frequently occurring competencies included knowledge of Indigenous methodologies, skills in alignment with relational approaches, characteristics such as humility and openness to learn new approaches, and social abilities. These KSAOs supported cognitions, decolonized practice, personal attributes, research, and relational dispositions associated with perceived readiness to practice. Conclusion: Research capacity for Indigenous health research is a complicated construct often left undefined or uninterrogated. This study helped to unpack research capacity for Indigenous health research for individuals to support evaluation, training, and selection.
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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 | 0.132 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".