Thoughts that breathe, and words that burn: poetic inquiry within health professions education
Bibliographic record
Abstract
Qualitative inquiry is increasingly popular in health professions education, and there has been a move to solidify processes of analysis to demystify the practice and increase rigour. Whilst important, being bound too heavily by methodological processes potentially represses the imaginative creativity of qualitative expression and interpretation-traditional cornerstones of the approach. Rigid adherence to analytic steps risks leaving no time or space for moments of 'wonder' or emotional responses which facilitate rich engagement. Poetic inquiry, defined as research which uses poetry 'as, in, [or] for inquiry', offers ways to encourage creativity and deep engagement with qualitative data within health professions education. Poetic inquiry attends carefully to participant language, can deepen researcher reflexivity, may increase the emotive impact of research, and promotes an efficiency of qualitative expression through the use of 'razor sharp' language. This A Qualitative Space paper introduces the approach by outlining how it may be applied to inquiry within health professions education. Approaches to engaging with poetic inquiry are discussed and illustrated using examples from the field's scholarship. Finally, recommendations for interested researchers on how to engage with poetic inquiry are made, including suggestions as to how to poetize existing qualitative research practices.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.136 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.027 | 0.103 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".