Determinants of treatment decisions in advanced dementia: a protocol for a cross-cultural mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: Values and preferences are key determinants of optimal care, and variability in patient values and preferences often dictates differences in patient management. Clinicians' views of patients' values and preferences may differ across cultural aspects and stage of training, but the extent to which this is the case remains uncertain. One key value and preference issue is the trade-off between quantity and quality of life, and this issue is particularly prominent among patients with dementia. We therefore propose to investigate the extent to which physicians' perceptions of optimal management for patients living with advanced dementia may differ due to cross-cultural factors and stage of medical training. METHODS AND ANALYSIS: We will conduct a sequential explanatory mixed-methods study (QUAN → qual). First, we will administer paper-based or electronic surveys during educational sessions, conferences and rounds to medical students, residents and physicians in ten countries, either in person or online. Following that, a qualitative inquiry, guided by the findings of the quantitative study and the principles of the interpretive description design, will inform an in-depth exploration of the predictive factors identified in the quantitative data analysis. ETHICS AND DISSEMINATION: The Hamilton Integrated Research Ethics Board at McMaster University has approved this study (approval number 2024-17651). We will disseminate our findings in peer-reviewed publications and present results at conferences as oral and poster presentations.
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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 | 0.121 | 0.092 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.083 | 0.015 |
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".