P079 Nurses educating patients about methotrexate: a video analysis study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».