Bibliographic record
Abstract
BACKGROUND: Effective data integration is a daunting task in mixed methods research. Several frameworks for data integration exist, but the choice of and the technique for integration depend upon the research question and design. Innovative integration techniques continuously need to be developed to tackle the integration challenge and provide alternative ways for researchers to generate plausible mixed meta-inferences. OBJECTIVES: The purpose of this study was to describe a new data analysis technique, tripartite analysis (TriPA), and illustrate its use in a convergent mixed-methods study. METHODS: This technique was developed based on a convergent mixed-methods study underpinned by dialectical pluralism aimed to understand Pakistani nursing students' perspectives about compassion and compassionate care and how these perspectives are consistent with the conceptualizations of compassion in nursing literature. RESULTS: TriPA entails analysis and integration using joint displays at three levels: case-by-case integrated analysis, separate and then merged quantitative and qualitative analysis, and comparative and integrated analysis of Levels I and II findings. DISCUSSION: TriPA can enable researchers to develop a more nuanced understanding of a given phenomenon through integration at various levels by identifying linkages within cases and across the whole data set and recognizing relational connections and emerging patterns.
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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 | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.011 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".