‘Decolonising’ the Clinical Encounter via Multi-Criteria Decision Support
Bibliographic record
Abstract
According to researchers drawing on the ideas of Jürgen Habermas, Canadian patients and Danish General Practitioners are both experiencing the 'colonisation' of their 'lifeworlds', though in different ways. Their suggested remedy is to ensure that the clinical encounter, freed of strategic rationality, prioritises Habermasian 'communicative action' aimed at mutual understanding. However, Blau argues that such communicative action can, and should be, inextricably interwoven with means-end rationality, rejecting Habermas' caricature of the latter. In agreement, but taking an operational perspective, we argue that decision support based on Multi-Criteria Decision Analysis can help produce the 'communicative means-end rationality' essential in a public health service based on role-respecting sincerity and autonomy.
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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 | 0.130 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".