Assessing research methodologies used to evaluate inequalities in end-of-life cancer care research: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: To provide equitable cancer care at the end of life, it is essential to first understand the evidence underpinning the existence of unequal cancer outcomes. Study design, measurement and analytical decisions made by researchers are a function of their social systems, academic training, values and biases, which influence both the findings and interpretation of whether inequalities or inequities exist. Methodological choices can lead to results with different implications for research and policy priorities, including where supplementary programmes and services are offered and for whom. The objective of this scoping review is to provide an overview of the methods, including study design, measures and statistical approaches, used in quantitative and qualitative observational studies of health equity in end-of-life cancer care, and to consider how these methods align with recommended approaches for studying health equity questions. METHODS AND ANALYSIS: This scoping review follows Arksey and O'Malley's expanded framework for scoping reviews. We will systematically search Medline, Embase, CINAHL and PsycINFO electronic databases for quantitative and qualitative studies that examined equity stratifiers in relation to end-of-life cancer care and/or outcomes published in English or French between 2010 and 2021. Two authors will independently review all titles, abstracts and full texts to determine which studies meet the inclusion criteria. Data from included full-text articles will be extracted into a data form that will be developed and piloted by the research team. Extracted information will be summarised quantitatively and qualitatively. ETHICS AND DISSEMINATION: No ethics approval is required for this scoping review. Results will be disseminated to researchers examining questions of health equity in cancer care through scientific publication and presentation at relevant conferences.
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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 | 0.280 | 0.264 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".