Person-Oriented Research Ethics and Dementia:The Lack of Consensus
Bibliographic record
Abstract
Research ethics extends beyond obtaining initial approval from research ethics boards. The previously established person-oriented research ethics framework provides guidelines for understanding ongoing ethics throughout the tasks of a research project, in a variety of research contexts. It focuses primarily on the relational and experiential aspects of research ethics, organized around five guideposts: (1) focus on researcher-participant relationships; (2) respect for holistic personhood; (3) acknowledgment of lived world; (4) individualization; and (5) empowerment in decision-making. Given the widespread impact of dementia and the ethical challenges dementia research presents, conducting meaningful, ethical research is of high importance. This review explores this person-oriented framework in the context of dementia by examining existing literature on ethics practices in dementia research. We use a critical interpretive literature review to examine publications from 2013 to 2017 for content related to the five guideposts of person-oriented research ethics. While there is much literature addressing the relational and experiential aspects of research ethics, there is a lack of unanimous conclusions and concrete suggestions for implementation. We compiled practical recommendations from the literature, highlighting tensions and suggesting furthering evidence-based ethics research fieldwork to construct an accessible, easy-to-use set of guidelines for researchers that will assist in putting person-oriented research ethics into practice in dementia 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 | 0.499 | 0.537 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.064 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 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".