Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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 it