An exploratory study using video analysis of rheumatology specialist nurses conducting methotrexate education consultations with patients
Bibliographic record
Abstract
BACKGROUND: Prior to commencing methotrexate, patients routinely attend an education consultation with a rheumatology nurse. The purpose of the consultation is to discuss the patients' expectations and concerns related to commencing methotrexate, the benefits of treatment, potential side effects and monitoring requirements. The aim of this study was to use video analysis to assess the structure, content and mode of delivery of the consultation. METHODS: Video recordings of 10 patient-nurse consultations, involving four specialist rheumatology nurses, were analysed and transcribed. The consultations were compared with the Calgary-Cambridge (CC) consultation model. Transcripts were thematically analysed. Data were quantitatively assessed for verbal and non-verbal behaviours. FINDINGS: Assessment of the video data using the CC model demonstrated good structure, content and flow of the consultation, influenced by the use of an information leaflet. Consultations generally consisted of communication from nurse to patient rather than a dialogue; the nurse spoke for 69%-86% of the time; clarification of the patient's understanding of the information did not take place in any of the consultations. Thematic analysis also showed that the nurse agenda dominated and the nurse was aware of 'overloading' the patient with information. Cues from the patients to discuss items of importance were often missed. CONCLUSION: Video analysis can be used to identify the aspects of the consultation that work well and those areas of the consultation that could be improved with specific training.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".