Formative evaluation of the video reflexive ethnography method, as applied to the physician–nurse dyad
Bibliographic record
Abstract
BACKGROUND: Despite decades of research and interventions, poor communication between physicians and nurses continues to be a primary contributor to adverse events in the hospital setting and a major challenge to improving patient safety. The lack of progress suggests that it is time to consider alternative approaches with greater potential to identify and improve communication than those used to date. We conducted a formative evaluation to assess the feasibility, acceptability and utility of using video reflexive ethnography (VRE) to examine, and potentially improve, communication between nurses and physicians. METHODS: We begin with a brief description of the institutional review boardapproval process and recruitment activities, then explain how we conducted the formative evaluation by describing (1) the VRE process itself; (2) our assessment of the exposure to the VRE process; and (3) challenges encountered and lessons learnt as a result of the process, along with suggestions for change. RESULTS: Our formative evaluation demonstrates that it is feasible and acceptable to video-record communication between physicians and nurses during patient care rounds across many units at a large, academic medical centre. The lessons that we learnt helped to identify procedural changes for future projects. We also discuss the broader application of this methodology as a possible strategy for improving other important quality and safety practices in healthcare settings. CONCLUSIONS: The VRE process did generate increased reflection in both nurse and physician participants. Moreover, VRE has utility in assessing communication and, based on the comments of our participants, can serve as an intervention to possibly improve communication, with implications for patient safety.
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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 | 0.239 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".