Identifying priorities for cancer caregiver interventions: protocol for a three-round modified Delphi study
Bibliographic record
Abstract
INTRODUCTION: Cancer is often considered a chronic disease, and most people with cancer have a caregiver, often a family member or friend who provides a significant amount of care during the illness trajectory. Caregivers are frequently in need of support, and a range of interventions have been trialled to improve outcomes. Consensus for optimal ways to support caregivers is not known. The aim of this protocol paper is to describe procedures for a modified Delphi study to explore expert consensus about important factors when developing caregiver interventions. METHODS AND ANALYSIS: Online modified Delphi methodology will be used to establish consensus for important caregiver intervention factors incorporating the Patient problem, Intervention, Comparison and Outcome framework. Round 1 will comprise a free-text questionnaire and invite the panel to contribute factors they deem important in the development and evaluation of caregiver interventions. Round 2 is designed to determine preliminary consensus of the importance of factors generated in round 1. The panel will be asked to rate each factor using a 4-point Likert-type scale. The option for panellists to state reasoning for their rating will be provided. Descriptive statistics (median scores and IQR) will be calculated to determine each item's relative importance. Levels of consensus will be assessed based on a predefined consensus rating matrix. In round 3, factors will be recirculated including aggregate group responses (statistics and comment summaries) and panellists' own round 2 scores. Panellists will be invited to reconsider their judgements and resubmit ratings using the same rating system as in round 2. This will result in priority lists based on the panel's total rating scores. ETHICS AND DISSEMINATION: Ethics for this study has been gained from the Deakin University Human Ethics Advisory Group. It is anticipated that the results will be published in peer-reviewed journals and presented in a variety of forums.
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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 | 0.095 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".