PL02.4.A SHARED DECISION-MAKING IN NEUROSURGICAL CONSULTATIONS WITH NEWLY DIAGNOSED BRAIN TUMOR PATIENTS: A THEMATIC ANALYSIS OF PATIENT INVOLVEMENT
Notice bibliographique
Résumé
Abstract BACKGROUND In shared decision-making (SDM) the patient serves as the expert on his or her own life and the health care professional serves as the medical expert. However, many SDM instruments that employ observer coding schemes primarily focus on information provision, and less on patient involvement. The items that are included on patient involvement are as follows: 1) introducing SDM to the patient, 2) providing personalized information and 3) discussing patient’s views, values and situation. These items may mark the presence of patient involvement and will be taken as a starting-point for this study, yet they do not provide information on how these items are discussed as part of the SDM process. The aim of this study is to illustrate the involvement of newly diagnosed brain tumor patients in SDM by analysing the decision-making process during neurosurgical consultations and identifying sub-themes related to patient involvement. MATERIAL AND METHODS This study employed a codebook thematic analysis to identify the sub-themes concerning aspects of patient involvement in the neurosurgical decision-making process. The first consultation between newly diagnosed glioma or meningioma patients and their neurosurgeons were audio-recorded, transcribed and coded. The study followed a recursive design continuing with analysis until data saturation was reached. RESULTS Saturation was reached after analysing 11 consultations. We identified 13 sub-themes within the three main themes, and we will support our findings with direct quotes from the consultations during the conference presentation. Theme 1 was comprised of the sub-themes inviting the patients to participate, presenting treatment options, inquiring about symptoms and daily life, asking patients about treatment preference and room for questions. Questions about patients’ personal circumstances are often asked near the end of a consultation, when the decision regarding treatment has already been made. Theme 2 was comprised of the sub-themes personalizing information, personalizing aspects of treatment and personalizing treatment recommendations. There are examples of neurosurgeons integrating information provided by the patient to personalize the decision-making process. Theme 3 was comprised of the sub-themes interference of disease, daily life, family and requested information. These sub-themes show what patients (spontaneously) share about their lives. CONCLUSION We illustrated how brain tumor patients contribute to the SDM process, what information patients and their decision-partners provide and how this information is integrated in the decision-making process during neurosurgical consultations. Our findings may provide handholds to further improve patient involvement and personalization of the decision-making process.
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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 | 0,033 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,007 | 0,008 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».