Enhancing decolonization and knowledge transfer in nursing research with non-western populations: examining the congruence between primary healthcare and postcolonial feminist approaches
Bibliographic record
Abstract
This article is a call for reflection from two distinct programs of research which converge on common interests pertaining to issues of health, social justice, and globalization. One of the authors has developed a research program related to the health and well-being of non-western populations, while the other author has expanded the field of Aboriginal and international research in Canada and abroad. Based on examples drawn from our respective programs of research, we suggest conciliating the philosophy of primary healthcare to postcolonial feminism for decolonizing research and enhancing knowledge transfer with non-western populations. We contend that applying the theoretical and methodological strengths of these two approaches is a means to decolonize nursing research and to avoid western neocolonization. In conciliating primary health care and postcolonial feminism, the goal is to enhance the pragmatic relevance of postcolonial feminism to generate resistance through transformative research for achieving social justice. In tapping into the synergistic and complementary epistemological assumptions of the philosophy of primary health care and postcolonial 'feminisms', nurse researchers reinforce the anti-oppresive goals of postcolonial feminist research. Consequently, this approach may enhance both decolonization and knowledge transfer through strategies like photovoice.
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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 | 0.226 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.111 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.004 | 0.050 |
| Research integrity | 0.004 | 0.007 |
| 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".