A shared treatment decision‐making approach between patients with chronic conditions and their clinicians: the case of diabetes
Bibliographic record
Abstract
In this paper, we discuss the Charles et al. approach to shared treatment decision-making (STDM) as applied to patients with chronic conditions and their clinicians. We perceive differences between the type of treatment decisions (e.g. end-of-life care, surgical treatment of cancer) that generated existing approaches of shared decision-making for acute care conditions (including the Charles et al. model) and the treatment decisions that patients with chronic conditions need to make and revisit on an ongoing basis. For instance, treatment decisions in the chronic care setting are more likely to require a more active patient role in carrying out the decision and to offer a longer window of opportunity to make decisions and to revisit and reverse them without important loss than acute care decisions. The latter may require minimal patient participation to realize, are often urgent, and may be irreversible. Given these differences, we explore the applicability of the Charles et al. model of STDM in the chronic care context, especially chronic care that relies heavily on patient self-management (e.g. diabetes). To apply the Charles et al. model in this clinical context, we suggest the need to emphasize the patient-clinician relationship as one of partners in making difficult treatment choices and to add a new component to the shared decision-making approach: the need for an ongoing partnership between the clinical team (not just the clinician) and the patient. In the last section of the paper, we explore potential healthcare system barriers to STDM in chronic care delivery. Throughout the discussion we identify areas for further research.
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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 | 0.042 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.012 | 0.017 |
| 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".