Mixing Qualitative and Quantitative Evidence in a Systematic Review
Bibliographic record
Abstract
Mixed studies reviews are literature reviews that use a systematic approach to combine quantitative, qualitative, and mixed methods studies. Mixed studies reviews are guided by the principles of mixed methods, specifically the integration of qualitative and quantitative evidence, with the goal of leveraging their complementarity. This chapter discusses and provides methodological guidance for mixed studies reviews in information science. This contribution is valuable since empirical research in information science typically involves diverse data collection and analysis methods and many research topics can be described as complex phenomena – both cases for which the mixed studies approach is recommended. This chapter provides a detailed description of the steps involved in a mixed studies review (question formulation, eligibility criteria, identification, selection, critical appraisal, data extraction, and synthesis) and illustrates each step with a concrete example from library and information science.
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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 | 0.525 | 0.733 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.042 | 0.035 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".