Bibliographic record
Abstract
Communication with family members in critical care is challenged by socioeconomic, environmental, and organizational factors. Ineffective communication between health care providers and family members results in psychological distress and anxiety among family members and can lead to misunderstanding of the patient’s condition and ineffective decision-making. This manuscript aims to explore barriers to effective communication, understand standardized communication tools, and support their implementation in critical care. An extensive search of various databases provided a variety of articles meeting the criteria of communication barriers in critical care, end-of-life, and strategies to overcome these barriers. Health literacy, diversity, and environmental factors are significant barriers to communication in critical care. The COVID-19 pandemic has further complicated communication, necessitating organizations to implement creative communication strategies. An effective strategy that is consistently identified for improving communication is the implementation of communication skills training. The READY framework, VALUE (Value, Acknowledge, Listen, Understand, and Elicit) guide, and Psychosocial Assessment and Communication Evaluation (PACE) tool are presented as frameworks to improve communication in critical care, and important elements of family meetings are identified. The collaborative efforts of the health care team and organization are essential in overcoming the specific challenges of communicating in critical care. Health care organizations and individuals are obligated to ensure that health care providers are appropriately trained, provided adequate resources, and are competent in communicating complex information with family members. Keywords: Family communication, critical care, communication training, communication framework, communication barrier
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".