Assessing Patient Perspectives on Receiving Bad News: A Survey of 1337 Patients With Life-Changing Diagnoses
Bibliographic record
Abstract
BACKGROUND: Guidelines for breaking bad news are largely directed at and validated in oncology patients, based on expert opinion, and neglect those with other diagnoses. We sought to determine whether existing guidelines for breaking bad news, particularly SPIKES, are consistent with patient preferences across patient populations. METHODS: Patients from an online community responded to 5 open-ended and 11 Likert-scale questions identifying their preferences in having bad news delivered. Patient participants received a diagnosis of cancer, lupus, amyotrophic lateral sclerosis, multiple sclerosis, HIV/AIDS, or Parkinson's disease. Additionally, we surveyed all 14 English-curriculum Canadian medical schools regarding resources used to teach breaking bad news. RESULTS: Ten of 12 responding schools used the SPIKES model. Preferences of 1337 patients were consistent with the recommendations of SPIKES. There was one exception: Most patients disagree that empathetic physical touch is important and some described apprehension. Responses were consistent across disease states. Content analysis of 220 open-ended patient responses revealed 16 patient-important themes. Themes were largely addressed by the SPIKES guidelines, but five were not: ensuring timely follow-up is planned; offering informational sheets about the diagnosis; offering contact information of support organizations, with some patients preferring patient support groups while others preferring counselors; and conveying a sense of determination to aid the patient through the diagnosis. The four most patient-important components of SPIKES were physicians conveying empathy, taking their time, explaining the diagnosis and its implications, and asking the patient if they understand. CONCLUSION: SPIKES is the most commonly taught framework for breaking bad news in Canadian medical schools. This is the first work to demonstrate that the existing guidelines in breaking bad news such as SPIKES largely reflect the perspectives of many patient groups, as assessed by quantitative and qualitative measures. We highlight the most important components of SPIKES to patients and identify five additional suggestions to aid clinicians in breaking bad news.
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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 | 0.003 | 0.014 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".