Complex health interventions in complex systems: improving the process and methods for evidence-informed health decisions
Bibliographic record
Abstract
The 2030 Agenda for Sustainable Development calls for real transformation, recognising that health goes beyond survival to include human rights, equity and the empowerment of vulnerable populations, including women and children.1 This Agenda demands strategies to address the underlying causes of ill health and inequity to achieve sustained improvements in health by ensuring healthy lives and promoting well-being for all at all ages. Within this context, governments and programmes struggle to make evidence-informed decisions to achieve these ambitious goals, while embracing these values. Current processes for developing evidence-informed guidance in public health encompass scoping and formulation of key questions; evidence retrieval, synthesis and appraisal; and the formulation of recommendations. These methods were originally conceived for clinical interventions as part of the evidence-based medicine movement.2 In public health these processes are applied to a broad range of health interventions implemented across varied health systems and contexts where a myriad of factors act both directly and indirectly to impact health and broader societal outcomes. Importantly, policy-makers pose questions beyond those of efficacy and safety and need guidance on the best ways to deliver interventions. Thus developers of evidence-informed guidance often apply processes and methods designed originally for assessing the comparative effectiveness of clinical interventions that are ill-adapted to formulating recommendations on highly context-dependent public health and health system interventions. A core function of World Health Organization (WHO) is to develop guidelines that set forth recommendations designed to support policy-makers and programme managers, particularly in low-income and middle-income countries, in making informed decisions about clinical practice or public health issues. WHO follows a transparent and rigorous process for developing evidence-informed guidelines.3 However, this process currently does not give adequate consideration to relevant aspects of complexity in health interventions or to interventions delivered in complex systems where outcomes occur at the …
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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 | 0.558 | 0.668 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.034 | 0.050 |
| Open science | 0.018 | 0.028 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".