Quasi-experimental study designs series—paper 5: a checklist for classifying studies evaluating the effects on health interventions—a taxonomy without labels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to extend a previously published checklist of study design features to include study designs often used by health systems researchers and economists. Our intention is to help review authors in any field to set eligibility criteria for studies to include in a systematic review that relate directly to the intrinsic strength of the studies in inferring causality. We also seek to clarify key equivalences and differences in terminology used by different research communities. STUDY DESIGN AND SETTING: Expert consensus meeting. RESULTS: The checklist comprises seven questions, each with a list of response items, addressing: clustering of an intervention as an aspect of allocation or due to the intrinsic nature of the delivery of the intervention; for whom, and when, outcome data are available; how the intervention effect was estimated; the principle underlying control for confounding; how groups were formed; the features of a study carried out after it was designed; and the variables measured before intervention. CONCLUSION: The checklist clarifies the basis of credible quasi-experimental studies, reconciling different terminology used in different fields of investigation and facilitating communications across research communities. By applying the checklist, review authors' attention is also directed to the assumptions underpinning the methods for inferring causality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.182 | 0.453 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it