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
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
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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 | 0.119 | 0.115 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".