A framework for selecting data generation strategies in qualitative health research studies
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
BACKGROUND: Qualitative health research has the potential to answer important applied health research questions to inform nutrition and dietetics practice, education and policy. Qualitative health research is a distinct subdiscipline of qualitative inquiry that purposefully draws upon the context of healthcare and emphasises health and wellness. METHODS: Qualitative health research is defined by two parameters: (1) the focus of the study and (2) the methods used. When considering the methods to be used, decisions are required about the type of data to be generated (e.g., transcripts, images and notes) and the process involved in data generation (e.g., interviews, elicitation strategies and observations) to answer the research question(s). Drawing upon examples from nutrition and dietetics literature, this paper provides a framework to support decision-making for nutrition and dietetics researchers and clinician researchers designing conducting qualitative health research. RESULTS: The guiding questions of the framework include: What types of data will be generated? Who is involved in data generation? Where will data generation occur? When will data generation occur? How will data be recorded and managed? and How will participants' and researchers' emotional safety be promoted? CONCLUSION: Questions about the types of data, those involved, where and when, as well as how safety can be maintained in data generation, not only support a more robust design and description of data generation methods but also keep the person at the centre of the research.
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 | 0.024 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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