Reimagining Indigenous healthcare through a readiness to practice lens: A quantitative content analysis of the empirical literature
Bibliographic record
Abstract
OBJECTIVES: The concept of "readiness to practice" has not been clearly delineated within an Indigenous health context. This systematic review occurred on a multi-database survey of published primary literature. The primary objective of this review was to determine what it takes for clinicians to be ready to practice with Indigenous populations. METHODS: This review identified articles published in the last 20 years within Canada, the United States, New Zealand, and Australia. The databases that were searched included CINAHL, Medline (via Ovid), Embase (via Ovid), Scopus, and Web of Science, with an additional hand search of references from relevant articles. This search took place from January to May 2022, with subsequent analysis from May to September 2022. RESULTS: Primary studies were coded using quantitative content analysis procedures and quantified codes were subjected to exploratory factor analyses. Four factors described a competent clinician across studies, including a relational disposition, decolonized practice, cultural immersion, and Indigenous professional support. CONCLUSION: This sphere of literature is relatively novel and there do not appear to be many individuals directly commenting on attributes needed to be prepared to work with Indigenous communities. There exist potential gaps in knowledge that could be addressed by conversations with Indigenous stakeholders and implementation of health education programs that focus on developing Indigenous-specific competencies.
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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 | 0.078 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".