The patient–physician interaction as a meeting of experts: one solution to the problem of patient non‐adherence
Bibliographic record
Abstract
Patient non-adherence is a common and important concern in clinical medicine. Some cases of patient non-adherence are cases in which the patient disagrees with the physician's recommended treatment based on particular reasons. Drawing upon science and technology studies literature, specifically the discussion by Collins and Evans and Wynne of how best to understand scientific controversies, I relate their ideas to the analogous conflict that may occur within a clinical interaction. I draw upon their recognition of the importance of contributory expertise and interactional expertise in providing legitimate knowledge. I also draw upon Wynne's idea of the 'negotiation of meanings' as an important element of the clinical interaction. To resolve potential conflicts between patient and physician before they develop into 'non-adherence', I propose the implementation of a new epistemological framework that recognizes legitimate knowledge offered by the patient as well as the physician. By situating this patient expertise framework within the paradigm of patient-centred medicine, and by assuming the goal of medical treatment to be treatment of suffering, patient expertise becomes centralized as a means of determining the nature of patient suffering. Two aspects of the patient's tacit knowledge - the body aspect and the meaning aspect - both of which are context-dependent and directly accessible only to the patient, are thus recognized as knowledge essential to the success of the interaction. The physician's role becomes that of both medical expert and possessor of 'interactional expertise', by which the physician recognizes and includes patient expertise in the treatment decision. By recognizing and incorporating the negotiation of meanings into the development of a treatment plan, this epistemological model of patient expertise should prevent cases of non-adherence based on disagreement.
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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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.047 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.030 | 0.029 |
| 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".