Life-threatening conditions, family conferences and patient-centered care: the experience of the PEDCONF
Bibliographic record
Abstract
ABSTRACT Introduction: Communication among healthcare professionals (HCPs), patients, and family members during hospital admission represents a major challenge for everyone involved. Life-threatening conditions or illnesses bring complexity to care and require effective and empathetic communication, one of the core components of patient-centered care. Nevertheless, there is still a lack of structured communication models in Portuguese to support HCPs, patients, and family members during family conferences in critical care units in Brazil. Experience Report: A structured and proactive communication model that uses the acronym PEDCONF was developed, consisting of four stages: (P) preparation of the conference, (E) early active listening to family members, (D) discussion of the clinical context, and (CONF) convergence between family and HCPs to develop a shared care plan. This model has been applied in the teaching-learning activities with HCPs through simulated conferences. Discussion: Although the implementation of structured and proactive communication models with family members of critically-ill patients has the potential to improve HCPs confidence in life-threatening illness scenarios, studies evaluating self-perceived outcomes related to confidence and performance during conferences by students in the short and long term are required in a local context. Conclusion: The PEDCONF model is presented as a promising tool to improve communication between HCPs, patients suffering from life-threatening diseases and their families, providing a shared and harmonized care plan among all those involved.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".