Decision tree for identifying pertinent integration procedures and joint displays in mixed methods research
Bibliographic record
Abstract
AIMS: To propose a decision tree for identifying appropriate integration procedures and joint displays for achieving integration in mixed methods studies. DESIGN: A methodological discussion. DATA SOURCES: Methodological literature including mixed methods textbooks, methodological reviews and studies published in the last 10 years (2012-2022). IMPLICATIONS FOR NURSING: Mixed methods are instrumental to study complex nursing care processes and health-human phenomena. Nurse researchers can use this decision tree to choose the most appropriate integration procedures to overcome the integration challenge when designing and conducting mixed methods nursing studies. CONCLUSION: Integration procedures and joint displays are the most widely used methods for tackling the integration challenge in mixed methods research (MMR). The multifaceted and contingent nature of these methods are beneficial for their tailored and adapted use at the data collection, analysis, interpretation and reporting levels. The use of the most pertinent integration procedures and joint displays is critical for ensuring quality in MMR. IMPACT: A growing methodological literature on MMR offers a wide range of integration procedures and techniques. Therefore, choosing appropriate integration procedures and analysis methods can be challenging for nurse researchers interested in conducting mixed methods studies. A decision tree is developed outlining 14 integration procedures and their corresponding mixed methods designs, purposes and joint displays. Examples of mixed methods studies in the discipline of nursing are presented to illustrate the implementation of the integration procedures. The decision tree can serve as a straightforward methodological tool for decision making in MMR. Nurse researchers can effectively use this decision tree for research and teaching purposes. PATIENT OR PUBLIC CONTRIBUTION: No direct patient or public contribution.
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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 | 0.292 | 0.456 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".