Information provision in life-threatening illnesses: comprehensive framework
Bibliographic record
Abstract
BACKGROUND: In life-threatening illnesses, open information provision can benefit patients and families. However, not all patients prefer to have all information. There is a lack of clinical guidance on how to handle patient preferences for non-disclosure. AIM: To develop a conceptual framework and practical guidance for clinicians regarding the spectrum of patients' information provision preferences with a focus on when patients do not desire to have full information. METHODS: Multidisciplinary expert stakeholder meeting. RESULTS: 20 expert stakeholders from various disciplines and continents participated in the expert meeting. Based on the qualitative results, a conceptual framework was created. Our framework highlights that information is never value-free but attains value via healthcare provider and patient/family factors, including how information is interpreted by clinicians and patients/families. In this process, ethical and sociocultural tensions can arise, such as between patient and family autonomy, that can influence harmful effects of the attained value of information along several axes such as empowerment versus disempowerment. To mitigate tensions and minimise harm, our framework produces practical guidance for clinicians such as making a connection and having an open attitude. CONCLUSIONS: Our framework has clinical, research and policy implications and can be further refined and tested. Ultimately, it serves as a starting point to reduce social and cultural inequities in end-of-life care information in a global context.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".