Joint displays for qualitative-quantitative synthesis in mixed methods reviews
Bibliographic record
Abstract
Mixed methods reviews offer an excellent approach to synthesizing qualitative and quantitative evidence to generate more robust implications for practice, research, and policymaking. There are limited guidance and practical examples concerning the methods for adequately synthesizing qualitative and quantitative research findings in mixed reviews. This paper aims to illustrate the application and use of joint displays for qualitative and quantitative synthesis in mixed methods reviews. We used joint displays to synthesize and integrate qualitative and quantitative research findings in a segregated mixed methods review about male nursing students' challenges and experiences. In total, 36 qualitative, six quantitative, and one mixed-methods study was appraised and synthesized in the review. First, the qualitative and quantitative findings were analyzed and synthesized separately. The synthesized findings were integrated through tabular and visual joint displays at two levels of integration. At the first level, a statistics theme display was developed to compare the synthesized qualitative and quantitative findings and the number of studies from which the findings were generated. At the second level, the synthesized qualitative and quantitative findings supported by each other were integrated to identify confirmed, discordant, and expanded inferences using generalizing theme display. The use of two displays allowed in a robust and comprehensive synthesis of studies. Joint displays could serve as an excellent method for rigorous and transparent synthesis of qualitative and quantitative findings and the generation of adequate and relevant inferences in mixed methods reviews.
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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 | 0.491 | 0.720 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".