Conference Report: Teaching Against the Grain: The Challenges of Teaching Qualitative Research in the Health Sciences. A National Workshop on Teaching Qualitative Research in the Health Sciences
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
This essay reflects on the proceedings of an invitational workshop on the nature and challenges of teaching qualitative research (QR) in health science settings. The context of this workshop is the increasing interest in QR in the health sciences and the inadequacy of pedagogy and institutional support for QR. We argue that there are special problems associated with teaching in an environment that embraces numerically based forms of knowledge and marginalizes unconventional research. Changes in the health research environment (e.g. applied research funding) and in the university environment (e.g. faster and briefer training) do not mesh easily with core premises of QR and can have a homogenizing, "dumbing down" effect on teaching. Teaching across wide disciplinary and professional divides, and among students with little or no social theory, can promote teaching QR as procedure, and at the lowest common denominator. Teachers must deal with the disruptive effects on students and other faculty of the critical dimensions of QR, and manage the structural constraints and political demands of thesis supervision. Despite the challenges of teaching "against the grain," the rewards and promise of teaching qualitative research in such environments remain, and we call for further discussion and leadership in this area. URN: urn:nbn:de:0114-fqs0502427
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 | 0.558 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.057 | 0.018 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.021 |
| 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