Methodological guidance for the conduct of mixed methods systematic reviews
Bibliographic record
Abstract
OBJECTIVE: The objective of this paper is to outline the updated methodological approach for conducting a JBI mixed methods systematic review with a focus on data synthesis; specifically, methods related to how data are combined and the overall integration of the quantitative and qualitative evidence. INTRODUCTION: Mixed methods systematic reviews provide a more complete basis for complex decision-making than that currently offered by single method reviews, thereby maximizing their usefulness to clinical and policy decision-makers. Although mixed methods systematic reviews are gaining traction, guidance regarding the methodology of combining quantitative and qualitative data is limited. In 2014, the JBI Mixed Methods Review Methodology Group developed guidance for mixed methods systematic reviews; however, since the introduction of this guidance, there have been significant developments in mixed methods synthesis. As such, the methodology group recognized the need to revise the guidance to align it with the current state of knowledge on evidence synthesis methodology METHODS:: Between 2015 and 2019, the JBI Mixed Methods Review Methodology Group undertook an extensive review of the literature, held annual face-to-face meetings (which were supplemented by teleconferences and regular email correspondence), sought advice from experts in the field, and presented at scientific conferences. This process led to the development of guidance in the form of a chapter in the JBI Manual for Evidence Synthesis, the official guidance for conducting JBI systematic reviews. In 2019, the guidance was ratified by the JBI International Scientific Committee. RESULTS: The updated JBI methodological guidance for conducting a mixed methods systematic review recommends that reviewers take a convergent approach to synthesis and integration whereby the specific method utilized is dependent on the nature/type of questions that are posed in the systematic review. The JBI guidance is primarily based on Hong et al. and Sandelowski's typology on mixed methods systematic reviews. If the review question can be addressed by both quantitative and qualitative research designs, the convergent integrated approach should be followed, which involves data transformation and allows reviewers to combine quantitative and qualitative data. If the focus of the review is on different aspects or dimensions of a particular phenomenon of interest, the convergent segregated approach is undertaken, which involves independent synthesis of quantitative and qualitative data leading to the generation of quantitative and qualitative evidence, which are then integrated together. CONCLUSIONS: The updated guidance on JBI mixed methods systematic reviews provides foundational work to a rapidly evolving methodology and aligns with other seminal work undertaken in the field of mixed methods synthesis. Limitations to the current guidance are acknowledged, and a series of methodological projects identified by the JBI Mixed Methods Review Methodology Group to further refine the methodology are proposed. Mixed methods reviews offer an innovative framework for generating unique insights related to the complexities associated with health care quality and safety.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.662 | 0.828 |
| Meta-epidemiology (narrow) | 0.006 | 0.011 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.033 | 0.029 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.013 | 0.015 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.029 | 0.022 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".