Caregiver Engagement in Serious Illness Communication in a Long-Term Acute Care Hospital Setting
Bibliographic record
Abstract
CONTEXT: Prolonged management of critical illnesses in long-term acute care hospitals (LTACH) makes serious illness communication (SIC), a clinical imperative. SIC in LTACH is challenging as clinicians often lack training and patients are typically unable to participate-making caregivers central. OBJECTIVES: This qualitative descriptive study characterized caregiver engagement in SIC encounters, while considering influencing factors, following the implementation of Ariadne Labs' SIC training at a LTACH in the Northeastern United States. METHODS: Clinicians' documented SIC notes (2019-2020) were analyzed using directed content analysis. Codes were grouped into four categories generated from two factors that influence SIC-evidence of prognostic understanding (yes/no) and documented preferences (yes/no)-and caregiver engagement themes identified within each category. RESULTS: Across 125 patient cases, 251 SIC notes were analyzed. In the presence of prognostic understanding and documented preferences, caregivers acted as upholders of patients' wishes (29%). With prognostic understanding but undocumented preferences, caregivers were postponers of healthcare decision-making (34%). When lacking prognostic understanding but having documented preferences, caregivers tended to be searchers, intent on identifying continued treatment options (13%). With poor prognostic understanding and undocumented preferences, caregivers were strugglers, having difficulty with the clinicians or family unit over healthcare decision-making (21%). CONCLUSION: The findings suggest that two factors-prognostic understanding and documented preferences-are critical factors clinicians can leverage in tailoring SIC to meet caregivers' SIC needs in the LTACH setting. Such strategies shift attention away from SIC content alone toward factors that influence caregivers' ability to meaningfully engage in SIC to advance healthcare decision-making.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".