Bibliographic record
Abstract
The interpretive and subjective nature of qualitative research has led to growing utilization of arts-based strategies for data collection, analysis and dissemination. The defining characteristic of all such strategies is that they are largely subjective and intended to invoke personal responses in the ‘audience.’ Following that direction, many qualitative researchers are using metaphor to capture themes emerging from their analysis. In this article, we explore ethical aspects of using metaphor in describing results of qualitative health research and illustrate some of the complexities using a case study of research conducted by one of the authors. Our analysis is designed to sensitize researchers and ethics reviewers to some unique ethical issues inherent to this approach towards data analysis and presentation. Issues related to participant dignity, respect and vulnerability led us to suggest that researchers should take these points into consideration in designing their research and seeking informed consent. Metaphors can be linguistic devices, but also conceptual aids that help develop patterns in analysis or that facilitate re-interpretation. However, there is a thin line between artistic licence for better expression and distorting the participants’ actual experience and meanings. Researchers, and reviewers, must be aware of the danger to participant dignity and integrity when aesthetics overshadow actuality. The use of metaphor may also trigger tensions between researchers and participants, especially if member checking is used. The implications of participant withdrawal must be considered and conveyed to ethics reviewers and participants. It is important to have a plan in place for dealing with some of these issues. These should be detailed in the proposal and communicated to participants. Institutional research ethics boards should, on their part, be prepared to ask questions if such details are lacking in the proposal.
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.
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 | 0.259 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.171 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".