Breaking Bad News: Effective Communication in Cancer Diagnosis Disclosure
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".