On the Path to Decolonizing Health Care Services: The Role of Marketing
Bibliographic record
Abstract
Despite considerable investment, health outcomes for First Nations people are well below those of the rest of the population in several countries, including Canada, the United States, and Australia. In this article, the authors draw on actor-network theory and the case of Birthing on Country, a successful policy initiative led by First Nations Australians, to explore the decolonization of health services. Using publicly available archival data and the theoretical guidance of actor-network theory, the analysis offers insight into how marketing techniques and technologies can be deployed to achieve improved health outcomes and implement decolonized approaches. The insights provided have theoretical implications for marketing scholarship, social implications for understanding and implementing an agenda of decolonization, and practical implications for health care marketing.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.082 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 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".