How Do Veterinary Students Engage When Using Creative Methods to Critically Reflect on Experience? A Qualitative Analysis of Assessed Reflective Work
Bibliographic record
Abstract
Critical reflection-the exploration and questioning of one's experience, beliefs, assumptions, and actions-supports resilience, empathy, the management of uncertainty, and professional identity formation. Yet for many students and educators, the techniques to engage in critical reflection are elusive. Creative methods that foster engagement with emotional and uncertain aspects of experience reportedly help some students to reflect at a more critical level than when they use reflective writing, and this study explores more deeply the experiences of such students, who used creative methods to critically reflect on challenging or troubling past events. A narrative methodology was utilized, in which researchers collaboratively co-constructed an understanding of students' experiences of reflection to identify the activities and steps they used. Creative methods did not inherently lead to critical reflection, but when this was achieved, the creative approaches seemed to facilitate a staging of reflection, which incorporated five sequential stages: preplanning creative depiction, experimenting with different ideas, deliberately completing the reflective piece, reflecting on creative work, and reflecting again on learning and development. This cyclic, repeated revisit to experience, as students engaged in each stage of their work, appeared to facilitate both a deep connection with the emotional elements of experience and a more distanced analysis. This ultimately led to a deepening of understanding of events, including the construction of students' own beliefs and empathy with others' views.
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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 | 0.034 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".