‘Conditional candour’ and ‘knowing me’: an interpretive description study on patient preferences for physician behaviours during end-of-life communication
Bibliographic record
Abstract
OBJECTIVE: To understand patients' preferences for physician behaviours during end-of-life communication. METHODS: We used interpretive description methods to analyse data from semistructured, one-on-one interviews with patients admitted to general medical wards at three Canadian tertiary care hospitals. Study recruitment took place from October 2012 to August 2013. We used a purposive, maximum variation sampling approach to recruit hospitalised patients aged ≥55 years with a high risk of mortality within 6-12 months, and with different combinations of the following demographic variables: race (Caucasian vs non-Caucasian), gender and diagnosis (cancer vs non-cancer). RESULTS: A total of 16 participants were recruited, most of whom (69%) were women and 70% had a non-cancer diagnosis. Two major concepts regarding helpful physician behaviour during end-of-life conversations emerged: (1) 'knowing me', which reflects the importance of acknowledging the influence of family roles and life history on values and priorities expressed during end-of-life communication, and (2) 'conditional candour', which describes a process of information exchange that includes an assessment of patients' readiness, being invited to the conversation, and sensitive delivery of information. CONCLUSIONS: Our findings suggest that patients prefer a nuanced approach to truth telling when having end-of-life discussions with their physician. This may have important implications for clinical practice and end-of-life communication training initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".