Protocol for the development and multisite validation of the Quality of Dying and Death-Revised Global Version scale
Bibliographic record
Abstract
INTRODUCTION: Evaluating the quality of dying and death is essential to ensure high-quality end-of-life care. The Quality of Dying and Death (QODD) scale is the best-validated measure of the construct, but many items are not relevant to participants, particularly in low-resource settings. The aim of this multisite cross-sectional study is to develop and validate the QODD-Revised Global Version (QODD-RGV), to enhance ease of completion and relevance in higher-resource and lower-resource settings. METHODS AND ANALYSIS: This study will be a two-arm, multisite evaluation of the cultural relevance, reliability and validity of the QODD-RGV across four participating North American hospices and a palliative care site in Malawi, Africa. Bereaved caregivers and healthcare providers of patients who died at a participating North American hospice and bereaved caregivers of patients who died of cancer at the Malawian palliative care site will complete the QODD-RGV and validation measures. Cognitive interviews with subsets of North American and Malawian caregivers will assess the perceived relevance of the scale items. Psychometric evaluations will include internal consistency and convergent and concurrent validity. ETHICS AND DISSEMINATION: The North American arm received approval from the University Health Network Research Ethics Board (21-5143) and the University of North Carolina Institutional Review Board (21-1172). Ethics approval for the Malawi arm is being obtained from the University of North Carolina Institutional Review Board and the Malawian National Health Science Research Committee. Study findings will be disseminated through publication in peer-reviewed journals and conference 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 | 0.073 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.104 | 0.030 |
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".