Contextual equipoise: a novel concept to inform ethical implications for implementation research using randomised controlled trials in low- and middle-income countries
Bibliographic record
Abstract
The call for universal health coverage requires the urgent implementation and scale-up of interventions that are known to be effective, in resource-poor settings. Achieving this objective requires high-quality implementation research (IR) that evaluates the complex phenomenon of the influence of context on the ability to effectively deliver evidence-based practice. Nevertheless, IR for global health is failing to apply a robust, theoretically driven approach, leading to ethical concerns associated with research that is not methodologically sound. Inappropriate methods are often used in IR to address and report on context. This may result in a lack in understanding of how to effectively adapt the intervention to the new setting and a lack of clarity in conceptualising whether there is sufficient evidence to generalise findings from previous IR to a new setting, or if a randomised controlled trial (RCT) is needed. Some of the ethical issues arising from this shortcoming include poor-quality research that may needlessly expose vulnerable participants to research that has not been adapted to suit local needs and priorities, and the inappropriate use of RCTs that denies participants in the control arm access to treatment that is effective within the local context. To address these concerns, we propose a complementary approach to clinical equipoise for IR, known as contextual equipoise. We discuss challenges in the evaluation of context and also with assessing the certainty of evidence to justify an RCT. Finally, we describe methods that can be applied to improve the evaluation and reporting of context and to help understand if contextual equipoise can be justified or if significant adaptations are required. We hope our analysis offers helpful insight to better understand and ensure that the ethical principle of beneficence is upheld in the real-world contexts of IR in low-resource settings.
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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 | 0.757 | 0.830 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.007 | 0.088 |
| Scholarly communication | 0.025 | 0.040 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".