SUPPORT Tools for evidence-informed health Policymaking (STP) 9: Assessing the applicability of the findings of a systematic review
Bibliographic record
Abstract
This article is part of a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers. Differences between health systems may often result in a policy or programme option that is used in one setting not being feasible or acceptable in another. Or these differences may result in an option not working in the same way in another setting, or even achieving different impacts in another setting. A key challenge that policymakers and those supporting them must face is therefore the need to understand whether research evidence about an option can be applied to their setting. Systematic reviews make this task easier by summarising the evidence from studies conducted in a variety of different settings. Many systematic reviews, however, do not provide adequate descriptions of the features of the actual settings in which the original studies were conducted. In this article, we suggest questions to guide those assessing the applicability of the findings of a systematic review to a specific setting. These are: 1. Were the studies included in a systematic review conducted in the same setting or were the findings consistent across settings or time periods? 2. Are there important differences in on-the-ground realities and constraints that might substantially alter the feasibility and acceptability of an option? 3. Are there important differences in health system arrangements that may mean an option could not work in the same way? 4. Are there important differences in the baseline conditions that might yield different absolute effects even if the relative effectiveness was the same? 5. What insights can be drawn about options, implementation, and monitoring and evaluation? Even if there are reasonable grounds for concluding that the impacts of an option might differ in a specific setting, insights can almost always be drawn from a systematic review about possible options, as well as approaches to the implementation of options and to monitoring and evaluation.
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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 | 0.563 | 0.875 |
| Meta-epidemiology (narrow) | 0.008 | 0.010 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.082 | 0.065 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.010 | 0.029 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".