Using Emphasis-Purposeful Sampling-Phenomenon of Interest–Context (EPPiC) Framework to Reflect on Two Qualitative Research Designs and Questions: A Reflective Process
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
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.
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 | 0.052 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it