Critical theory and cultural competency in medical QI projects: lessons from Pacific Islander communities
Bibliographic record
Abstract
This reflection explores the transformative power of critical theory through a quality improvement (QI) project creating culturally relevant dietary resources for Pacific Islander communities in Washington State, USA. Food injustice and lack of food sovereignty are examined as manifestations of modern-day colonialism perpetuated by capitalist-driven social structures. The methodology employed critical reflection, defined as the process of examining assumptions and power relations that shape practice, central to critical theory. Iterative group discussions aimed to understand the impact of individual and collective assumptions, power dynamics, and oppression on the project's conceptualization, implementation, and evaluation.Specific recommendations are integrated into the discussion to aid those replicating similar protocols, emphasizing actionable steps such as engaging with the community at all project stages. The research team, composed of osteopathic medical students and a critical theory expert, engaged in reflexivity to understand how social locations and lived experiences influenced perceptions. Despite the goal of fostering cultural inclusivity, limitations in engaging the Pacific Islander community throughout the project lifecycle highlighted the need for cultural humility and participatory action methodology.This study underscores the importance of understanding the history and socio-political context of marginalized communities to avoid perpetuating colonial practices and trauma. It emphasizes the necessity for medical schools to incorporate sociological theories into curricula to promote compassionate, culturally appropriate care and research. By critically examining positionality and engaging in transformative learning, the group advocates for systemic changes towards a more equitable global healthcare system.Through critical reflection, the group has come to understand how lived experiences have shaped perceptions of oppression, which are entrenched within and perpetuated by social institutions. Actionable items from these reflections are presented to help future practitioners and educators apply cultural humility, community empowerment, and critical theory in QI projects.
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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 | 0.049 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.036 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.012 |
| 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".