Bibliographic record
Abstract
Rationale: Conventional models of cultural humility - even those extending analysis beyond the dyad of healthcare provider-patient to include concentric social influences such as families, communities and institutions that make the clinical relationship possible - aren’t conceptually or methodologically calibrated to accommodate shifts occurring in contemporary biomedical cultures. More complex models are required that are attuned to how advances in biomedical, communications and information technologies are increasingly transforming the very cultural and material conditions of health care and its delivery structures, and thus how power manifests in clinical encounters. Methodological Intervention: In this paper, we offer a two-pronged intervention in the cultural humility literature. At a first level of analysis, we suggest the need to broaden understandings of culture and associated workings of power to accommodate the effects of biomedicine’s technologising turn. A second level of intervention invites experimentation to broaden the availability of methodological tools to analyse and assess the multidimensionality of technologies and their agentic effects in healthcare encounters. Drawing from new materialism theories, practices of care are approached “diffractively” as contingent and dynamic material-discursive events. Our neo-materialist framework for cultural humility expands analytical sight-lines beyond hierarchical relationships and dichotomies privileging humans (practitioner and/or patient) as sole actants in the clinical exchange. Attended to are the ongoing dynamics of practices entangling big-data driven knowledges and interventions, pharmacological technologies and material instruments and devices, diseases, and the bodies/subjectivities of health care providers and patients. We investigate the implications for clinical assessment if a cultural humility framework is methodologically attuned to the clinical encounter as a discontinuous, discursive-material process producing multiple, contextually emergent data moments and objects for analysis. Engaging evaluative inquiry diffractively allows for a different ethical practice of care, one that attends to the forms of patient and health provider accountability and responsibility emerging in the clinical encounter.
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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 | 0.057 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.088 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".