Reflection in Professional Practice and Education in Engineering, Nursing, and Teaching
Bibliographic record
Abstract
Background: Critical reflection is an essential curricular component for learning from experience that determines placement quality in postsecondary experiential learning placements. However, there are poor empirical connections between the use of critically reflective processes and learning outcomes. Purpose: This research explored reflective processes professionals use in their practice and how these processes compare with the reflective activities postsecondary instructors in professional faculties use during experiential learning. Methodology/Approach: This collective case study used focus group interviews, field notes, and professional grey literature to examine the research questions. Findings/Conclusions: Professional training programs must align their reflective practices with more integrated and holistic models of reflective practice to better mirror the professional skills demanded in professional practice contexts. Professionals in context-laden professional environments should integrate reflective activities into their practice based on emergent, iterative, and cocreative models that are more like their lived realities at work. Reflective practices which better fit and mirror these lived realities may lead to better connections between reflective activities and work outcomes. Implications: Professional environments are complex, dynamic, and affected by contextual factors. New integrated and holistic models of reflective experience should replace the separated, stepwise, or automatic models that have guided reflective practices in the past.
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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 | 0.040 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".