The Pathway Building Technique in Implementation Research Using Mixed Methods Design
Bibliographic record
Abstract
BACKGROUND: Data integration refers to combining quantitative and qualitative data in mixed methods. It can be achieved through several integration procedures. The building integration procedure can be used for developing quantitative instruments by integrating data from the qualitative phase. There are limited examples of data integration using the building procedure in mixed methods and implementation science. PURPOSE: The purpose of this article is to illustrate how the pathway building technique can be used to integrate data in mixed methods research through concurrent use of implementation science models and frameworks. METHODS: This two pathway building technique was developed based on a mixed methods implementation project of developing implementation strategies to promote compassionate nursing care of complex patients. RESULTS: The first pathway is the integration of qualitative data from the first phase of mixed methods study with implementation models and frameworks to create a quantitative instrument (i.e., a Q-sort survey) for the subsequent phase. The second pathway is the operationalization of the Q-sort survey results (i.e., implementation strategies) using an implementation science specification framework. CONCLUSION: The pathway technique is valuable for mixed methods research and implementation science as it offers a theory-based innovative method to tackle integration challenge.
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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 | 0.282 | 0.237 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".