Indigenous Relationality and Kinship and the Professionalization of a Health Workforce
Bibliographic record
Abstract
We, as a group of academic learners, argue the professionalization of healthcare service providers reinforces hierarchies of knowledge that results in the exclusion of Indigenous Peoples. Through decolonizing theory and Indigenous methodology, we applied Indigenous understandings of relationality and kinship to examine the professionalization of the health workforce. Relationality is a philosophy that describes the interconnections between all of creation and kinship consists of family, community, and all extended human and more-than-human relations. Indigenous health knowledges reflect relationality and kinship and are practiced by midwives, doulas, and Comadronas. Within the Euro-Western biomedical model, these healers are often incorporated into maternity care services for the purposes of professionalizing their roles. Professionalization, however, reinforces power differentiations between healthcare providers and advances biomedical hegemony and hierarchies of knowledge, all of which exclude Indigenous kinship and relationality. The dangers of professionalization of the health workforce result in the omission of Indigenous knowledges, because the Euro-Western biomedical model of health is built on the philosophies of colonialism and capitalism. To counter professionalization, Indigenous relationality and kinship must be prioritized in the provision of healthcare so that it is inclusive to Indigenous Peoples and their knowledges, the results of which will benefit us all.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".