Using Emphasis-Purposeful Sampling-Phenomenon of Interest–Context (EPPiC) Framework to Reflect on Two Qualitative Research Designs and Questions: A Reflective Process
Bibliographic record
Abstract
A satisfactory research question often signifies the beginning point for many researchers. While this can be true for quantitative studies because of pre-defined research questions, qualitative research questions undergo series of revisions through a reflective process. This reflective process provides the framework for the subjectivity associated with qualitative inquiry. The continuous iterative reflective process is an essential component for developing qualitative research questions that correspond with the various qualitative study designs. Although qualitative inquiry is term exclusively subjective, there is a need to use a framework in developing qualitative research questions. The Emphasis- Purposeful sampling- Phenomenon of interest – Context (EPPiC) framework guides qualitative researchers in developing and revising qualitative research questions to suit a specific qualitative approach. This article addresses both the development of a research question using the “EPPiC framework” and demonstrate how to revise the “developed” research question to reflect two qualitative research design. I developed a qualitative research question for Sally Thorne’s Interpretive Description design using the EPPiC Framework and subsequently revised the research question to suit a grounded theory design.
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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 | 0.320 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".