Collaging Integration Procedure for Integrating Literature, Theory, and Research Data in Mixed Methods Research
Bibliographic record
Abstract
AIMS: To propose the collaging integration procedure for linking literature and theory to research data in mixed methods research (MMR) and illustrate its application in two mixed methods studies. DESIGN: Discussion paper/research methodology. DATA SOURCES: The collaging technique was used and developed based on two exploratory sequential nurse-led mixed-methods studies. RESULTS: The collaging technique entails using multiple artefacts (data fragments, figures and textual information) within one figure. Collaging can generate relevant pre-post linkages, create meaning and refine inferences and meta-inferences. CONCLUSION: This paper offers a novel integration technique for meaningful integration of the literature review and theoretical dimensions in the integration trilogy. IMPLICATIONS FOR NURSING: Nurse researchers can use the collaging integration procedure for effective integration for conducting rigorous mixed-methods research. Collaging is a straightforward yet effective technique for enhancing integration in the literature review and theoretical dimensions in MMR. IMPACT: Linking literature review, theory and research data facilitates a more meaningful interpretation of research findings. While researchers may be able to create a more fully integrated MMR design by integrating multiple dimensions of the study, to date, most of the empirical and methodological literature on MMR has focused on integration at the design, fieldwork, analysis and interpretation dimensions, ignoring others, such as the literature review and theoretical dimensions. Collaging enables intensive analysis of the raw data and embeds the insights gained from literature and theory throughout the data analysis and presentation, thereby avoiding neglecting insights which could have been gained by back-and-forth comparison and integration of literature review and theoretical underpinnings. 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 | 0.390 | 0.552 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".