Integrating the Illness Meaning and Experience of Patients: the McGill Illness Narrative Interview Schedule as a PCM Clinical Communication Tool
Bibliographic record
Abstract
Despite extraordinary progress in biomedical technology and health care services, there is growing criticism of the depersonalization of clinical practice and the limits of medical and professional knowledge. In Person-centered Medicine (PCM), one of the aims is to give systematic attention to the illness meaning and experience of patients and to integrate into medical care the wellbeing, autonomy, spirituality and dignity of patients of diverse cultural backgrounds. There is however a dearth of validated tools clinicians can use to implement a PCM approach during the clinical communication tasks of diagnosis, negotiation of treatment choice and preventive behaviors. In this paper, we will argue that an abbreviated version of the McGill Illness Narrative Interview (MINI) has the potential to be used during clinical communication to implement a PCM approach. This paper will discuss epistemological issues of lay and medical knowledge as well as the patient’s empowerment that need to be addressed in clinical communication. Examples of corresponding clinical communication challenges posed by various medical specialties will be discussed, along with a critical overview of the conceptual models used to guide clinical communication in a PCM manner. Finally we propose that the development and evaluation of a clinical version of the MINI could help address some important challenges to implementing a PCM approach in a clinical context.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".