Ethical analysis of vulnerabilities in cluster randomized trials involving people living with dementia in long‐term care homes
Bibliographic record
Abstract
Cluster randomized trials (CRT) of non-pharmacological interventions are an important means of improving the quality of care and quality of life of people living with dementia (PLWD) in long-term care (LTC) homes. PLWD in LTC homes are, however, vulnerable in manifold ways. Therefore, researchers require guidance to ensure that the rights and welfare of PLWD are protected in the course of this valuable research. In this article, we introduce a framework for identifying vulnerabilities in randomized trials and apply it to three CRTs involving PLWD in LTC homes. CRTs may render PLWD in LTC homes vulnerable to three autonomy wrongs: inadequately informed consent, inadequately voluntary consent, and invasions of privacy; two welfare wrongs: risks of therapeutic procedure exceed potential benefits, and excessive risk of non-therapeutic procedures; and one justice wrong: unjust impact of research activities on care. We then discuss appropriate, feasible additional protections that can be implemented to mitigate vulnerability while preserving the scientific validity of the CRT. Corresponding additional protections that can be feasibly implemented include capacity assessments, substitute decision-makers, assent, insulation from LTC home employees during the consent process, patient advocates, utilizing LTC home employees for data collection, stakeholder engagement, additional supervision during study procedures, using caregivers to complete questionnaires by proxy, and gatekeeper permission. Reassuringly, many of these additional protections promote, rather than imperil, the scientific validity of these trials.
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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 | 0.729 | 0.843 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.012 | 0.011 |
| 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".