Enhancing Workplace Learning: A Video Reflexive Ethnography Study
Bibliographic record
Abstract
INTRODUCTION: The clinical environment offers rich learning opportunities through activities and interactions. Yet, because workplace learning (WPL) is embedded in practice, it tends to be invisible. For clinical teachers and researchers, identifying what is learned and how to enhance learning is challenging. Video reflexive ethnography (VRE), an innovative observational methodology, can illuminate and enhance workplace learning processes. This study explored WPL using VRE to determine its insights and potential to enhance learning. METHODS: Conducted in a rural Australian GP setting, this study utilised VRE, a practice-based methodology. Participants, including medical students and GPs, engaged in video ethnography (Phase 1) and captured workplace learning encounters in brief video clips (Phase 2). Reflexive sessions followed, where participants appraised these videoed encounters (Phase 3). Framework analysis, informed by workplace learning theory, explored (1) the video excerpts to explore workplace learning affordances and (2) transcripts of the reflexive sessions to examine learners' and supervisors' learnings. RESULTS: Analysis identified how supervisors guided students' learning through debriefing, dialogue, and articulation of clinical reasoning. Students shared their insights about workplace learning processes and their roles. Supervisors (medical and allied health) deepened their understanding of supervision by reflecting on their own and others' practices through video analysis. DISCUSSION: This study suggests VRE is a feasible research approach that also enhances WPL. Observational and participatory research methods can make the tacit explicit and open to dialogue, offering valuable contributions to workplace learning research.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".