Using Stake’s Qualitative Case Study Approach to Explore Implementation of Evidence-Based Practice
Bibliographic record
Abstract
Although the use of qualitative case study research has increased during the past decade, researchers have primarily reported on their findings, with less attention given to methods. When methods were described, they followed the principles of Yin; researchers paid less attention to the equally important work of Stake. When Stake's methods were acknowledged, researchers frequently used them along with Yin's. Concurrent application of their methods did not take into account differences in the philosophies of these two case study researchers. Yin's research is postpositivist whereas Stake's is constructivist. Thus, the philosophical assumptions they used to guide their work were different. In this article we describe how we used Stake's approach to explore the implementation of a falls-prevention best-practice guideline. We focus on our decisions and their congruence with Stake's recommendations, embed our decisions within the context of researching this phenomenon, describe rationale for our decisions, and present lessons learned.
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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 | 0.078 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".