Bibliographic record
Abstract
Even the most skilled physician knows that conveying bad news to a patient or family can be one of the most arduous aspects of health care. Historically, breaking bad news is a skill that has generally been under-emphasized in training programmes. Critical care physicians are commonly faced with the difficult task of breaking bad news, often in dramatic, emotional and unexpected situations. Breaking Bad News is a website designed to provide all health practitioners (not just critical care physicians) with a framework, skills and some practical suggestions on how to approach this situation effectively, efficiently and with compassion. The website is divided into three main sections: guidelines, strategies and resources. The guidelines section comprehensively covers a large amount of material ranging from the logistics of 'getting started' all the way to 'planning and follow up'. Most of the subsections are also accompanied by one or two exercises, along with an example that emphasizes the intended content of that subsection. The guidelines end with a top 10 'do's and don'ts' portion, which is particularly directed and helpful. The strategies section focuses predominantly on the aftermath of the patient having received bad news. Small subsections succinctly cover the topics of 'denial', 'collusion', 'anger, guilt and blame', 'grief', 'encouraging hope' and 'answering difficult questions'. Each subsection discusses common scenarios that one could anticipate and possible solutions to those scenarios. The resources section is a UK directed portion of the website that offers further excellent resources for users. It provides printer ready handouts for patients, as well as the 10 'do's and don'ts' of breaking bad news in a printable pocket-card format. The best part of the resources section is the comprehensive list of more than 25 websites where patients and health care workers may further pursue information about specific hospital programmes, treatment programmes, support organizations and help groups. Targeted at the UK health care community, the resources section, although comprehensive, is limited in its usefulness to the international community. Breaking Bad News is supported by an unrestricted educational grant from Pfizer.
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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.204 | 0.142 |
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".