Breaking Bad News: Effective Communication in Cancer Diagnosis Disclosure
Notice bibliographique
Résumé
Doctors’ overall communication skills and their verbal methods of delivering diagnostic news greatly impact how patients view their illnesses and are therefore medically important in the context of Breaking Bad News (BBN). Despite the significant role of the language of diagnostic disclosure in this context and the linguistic variability observed in how doctors convey diagnoses, the exact language used by physicians in diagnostic disclosure has not been sufficiently studied. The present research investigation takes a step towards filling this gap through four studies which contribute to our understanding of BBN encounters. Acting as a pilot, Study 1 follows the goal of developing a preliminary Critical Discourse Analysis (CDA) framework for the study of simulated BBN encounters in Study 2. It uses data obtained from online educational videos for BBN training purposes to identify discursive strategies medical professionals are trained to use in conveying diagnostic news. The main goal of Study 2 is to develop, test, and finalize our novel CDA framework for the critical analysis of the language of diagnostic disclosure. The study also aims to use this framework to identify discursive patterns in conveying diagnostic news to cancer patients and to provide possible explanations for and implications of physicians’ choices of these patterns. Data for this Study are collected through 15 simulated doctor-patient consultation sessions conducted through the University of Saskatchewan’s Clinical Learning Resource Center (CLRC) and analyzed based on our CDA framework. Next, Study 3 aims to explore the BBN encounter from the perspective of physicians. It uses in-depth semi-structured interviews to provide insight into how physicians describe the BBN task, the challenges they face, what their BBN learning trajectories look like, and the importance they attribute to communication skills. The data are subsequently analyzed through Interpretative Phenomenological Analysis (IPA). To ensure that both doctors’ and patients’ voices are heard and as Receiving Bad News (RBN) is a pivotal event in an individual’s healthcare experience, Study 4 investigates cancer patients’ perspectives and experiences of this encounter. This study allows us to gain insight into the preferences of these individuals for discursive methods of diagnostic disclosure. An IPA approach is taken to explore how patients manage to understand their illness, what role their informing physician has in the process, and whether patients have any preferences for a communicative pattern of disclosure. The four studies conducted as part of this doctoral research project address the need for a framework through which medical discourse can be critically analyzed. While contributing to general knowledge of the practice of diagnostic disclosure, the studies also serve the interests of current and future physicians by allowing them to make conscious discursive choices through critical reflexivity and self-observation. The proposed novel model presented in this thesis will be useful in developing curricula, training programs, and workshops that will help medical students and practicing clinicians recognize their power and privilege in shaping the initial perceptions of illness by patients through their specific communicative strategies of diagnostic disclosure. Results of studies 1 and 2 reveal the types of discursive strategies most commonly employed by medical specialists in BBN, while studies 3 and 4 provide in-depth insight into the lived BBN/RBN experiences of physicians and their patients.
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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 | 0,024 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,018 |
| Communication savante | 0,013 | 0,013 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».