P079 Nurses educating patients about methotrexate: a video analysis study
Bibliographic record
Abstract
Abstract Background/Aims Education prior to starting therapeutic drugs is essential so that patients understand how to take them, what to expect in terms of effects and anticipated side effects, also for monitoring and supply requirements. The benefits of methotrexate can be delayed, tolerability problems are common, side effects can be severe and the drug is used in much bigger doses for cancer treatment which could complicate internet searches, therefore education is essential. Undertaking education is a fundamental role for Rheumatology nurses. We were interested to explore this interaction between nurse and patient using video recordings. Methods Recordings were conducted of nurses educating patients prior to starting methotrexate, for the first time. The recordings were downloaded and reviewed minute by minute and were scored against items of the Calgary Cambridge (C-C) consultation model on a 4-point scale: 0= no evidence; 1= needs development; 2= competent; 3= excellent. Additionally, transcripts were typed and analysed thematically. Videos were further analysed quantitatively for each utterance and body movement using the Medical Interactive Process System (MIPS). Results Ten recordings involving four nurses were made. The C-C assessment showed good structure, content and flow, driven by the use of an information leaflet. The nurses dominated the conversation speaking for between 69-86% of the time and involved the patient sparsely during the encounter, there was also little checking to ensure the patient understood the information being conveyed. Thematic analysis also showed that the nurse agenda dominated, and frequently brought the encounter back to the contents of the leaflet. Cues from the patients to discuss topics important to them, were often missed. Nurses recognised that they were often overloading the patient with information. The MIPS analysis showed that “giving information” dominated the nurse utterances and head nodding and assent by positive utterances dominated for the patient. Interestingly there was a lot more head nodding than positive utterances suggesting that head nodding was more about deference to the nurses perceived higher status rather than indicating understanding. Nurses in the higher scoring interviews on the C-C comparison made more illustrative gestures, asked more open questions with more checking and summarising and less interruptions. Patients in lower scoring interviews were more animated with gestures and head movements. They also checked information given and interrupted more. Conclusion Nurses are doing many things well but consultations could be improved with training aimed at improving patient participation, awareness of cues, checking and summarising understanding. Also, interpretation of body language could be improved. Nodding does not necessarily indicate understanding and an animated patient who interrupts and checks is probably not having their perspective addressed. Disclosure S. Robinson: None. N. Adams: None. J. Scott: None. C. Walker: None. A. Hassell: None. S. Ryan: None. D. Walker: None.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".