Ethical Principles Pertaining to the Care of People With Dementia: Protocol for a Qualitative Thematic Synthesis of Legal Documents
Bibliographic record
Abstract
BACKGROUND: The global prevalence of dementia presents profound challenges for health care systems, societies, and legal structures worldwide. While the ethical dimensions of dementia care have been extensively discussed in the literature, limited research addresses how ethical principles are effectively operationalized within legal frameworks governing dementia care. OBJECTIVE: We aim to explore how national, European, and international legal instruments integrate and translate ethical principles such as autonomy, dignity, beneficence, and justice into dementia care legal documents, including case law and legislation. METHODS: This study will be conducted by a team of 24 researchers from 15 European countries, within a European Union (EU)-funded European Cooperation in Science and Technology Action on dementia care. The project applies a combined methodological approach, using qualitative thematic synthesis and a legal document review framework. Legal documents published between 2010 and 2025 will be searched and qualitatively analyzed at 3 levels: national, EU, and international, including legislation, case law, and authoritative legal literature explicitly addressing dementia care. Researchers from each country will conduct searches in national legal databases using predefined search terms. At the EU and international levels, databases such as Westlaw, Lexis+, the European Court of Human Rights, and the European Ombudsman will be consulted. Key information from each document will be collected using a standardized data extraction tool, focusing on ethical principles and frameworks. Data analysis will combine deductive and inductive approaches, allowing the identification of ethical principles and the emergence of new concepts discussed in the documents. Thematic analysis will follow a 3-stage process: line-by-line coding, grouping codes into descriptive themes, and developing analytical themes to address the research questions. RESULTS: This study will identify and categorize ethical principles embedded in legal documents, analyzing their expression, interpretation, and variability across jurisdictions. The project is scheduled to be implemented throughout 2025. In January 2025, a pilot test of the data extraction tool was conducted. Between February 2025 and April 2025, a systematic search of legal documents meeting the inclusion criteria was performed, applying a structured 3-level search strategy. Data extraction and initial coding are planned from May 2025 to July 2025, followed by data analysis between September 2025 and October 2025, through iterative coding and collaborative discussion. The final research paper will be drafted in November 2025. CONCLUSIONS: This cross-country thematic synthesis will provide critical insights into how ethical principles guide dementia care and shape its discussion within legal systems. By systematically analyzing legal instruments through an ethical lens, this study aims to bridge the gap between ethical theory and legal practice, offering valuable guidance for future policy development. The findings will contribute to promoting ethically grounded and legally coherent frameworks for dementia care, safeguarding the rights and dignity of individuals living with dementia across diverse legal cultures. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71490.
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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 | 0.128 | 0.108 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.068 | 0.011 |
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".