Convergent and sequential synthesis designs: implications for conducting and reporting systematic reviews of qualitative and quantitative evidence
Bibliographic record
Abstract
BACKGROUND: Systematic reviews of qualitative and quantitative evidence can provide a rich understanding of complex phenomena. This type of review is increasingly popular, has been used to provide a landscape of existing knowledge, and addresses the types of questions not usually covered in reviews relying solely on either quantitative or qualitative evidence. Although several typologies of synthesis designs have been developed, none have been tested on a large sample of reviews. The aim of this review of reviews was to identify and develop a typology of synthesis designs and methods that have been used and to propose strategies for synthesizing qualitative and quantitative evidence. METHODS: A review of systematic reviews combining qualitative and quantitative evidence was performed. Six databases were searched from inception to December 2014. Reviews were included if they were systematic reviews combining qualitative and quantitative evidence. The included reviews were analyzed according to three concepts of synthesis processes: (a) synthesis methods, (b) sequence of data synthesis, and (c) integration of data and synthesis results. RESULTS: A total of 459 reviews were included. The analysis of this literature highlighted a lack of transparency in reporting how evidence was synthesized and a lack of consistency in the terminology used. Two main types of synthesis designs were identified: convergent and sequential synthesis designs. Within the convergent synthesis design, three subtypes were found: (a) data-based convergent synthesis design, where qualitative and quantitative evidence is analyzed together using the same synthesis method, (b) results-based convergent synthesis design, where qualitative and quantitative evidence is analyzed separately using different synthesis methods and results of both syntheses are integrated during a final synthesis, and (c) parallel-results convergent synthesis design consisting of independent syntheses of qualitative and quantitative evidence and an interpretation of the results in the discussion. CONCLUSIONS: Performing systematic reviews of qualitative and quantitative evidence is challenging because of the multiple synthesis options. The findings provide guidance on how to combine qualitative and quantitative evidence. Also, recommendations are made to improve the conducting and reporting of this type of review.
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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 | 0.865 | 0.951 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.033 | 0.043 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".