Feasibility and Acceptability of a Novel Intensive Care Unit Communication Intervention (“Let’s Talk”) and Initial Assessment Using the Multiple Goals Theory of Communication
Bibliographic record
Abstract
Background: Family members of intensive care unit (ICU) patients often report poor communication, feeling unprepared for ICU family meetings, and poor psychological outcomes after decision-making. The objective of this study was to create a tool to prepare families for ICU family meetings and assess feasibility of using Communication Quality Analysis (CQA) to measure communication quality of family meetings. Methods: This observational study was conducted at an academic tertiary care center in Hershey, PA from March 2019 to 2020. Phase 1a involved conceptual design. Phase 1b entailed acceptability testing of 2 versions of the tool (text-only, comic) with 9 family members of non-capacitated ICU patients; thematic analysis of semi-strucutred interviews was conducted. Phase 1c assessed feasibility of applying CQA to audio-recorded ICU family meetings (n = 17); 3 analysts used CQA to assess 6 domains of communication quality. Wilcoxon Signed Rank tests were used to interpret CQA scores. Results: Four themes emerged from Phase 1b interviews: participants 1) found the tool useful for meeting preparation and organizing thoughts, 2) appreciated emotional content, 3) preferred the comic form (67%), and 4) had indifferent or negative perceptions about specific elements. In Phase 1c, clinicians scored higher on the CQA content and engagement domains; family members scored higher on the emotion domain. CQA scores in the relationship and face domains had the lowest quality ratings. Conclusions: Let’s Talk may help families become better prepared for ICU family meetings. CQA provides a feasible approach to assessing communication quality that identifies specific areas of strengths and weaknesses in communication.
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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 | 0.036 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".