Assessing Family Members' Satisfaction with Information Sharing and Communication during Hospital Care at the End of Life
Bibliographic record
Abstract
CONTEXT: Despite the fact that most deaths occur in hospital, problems remain with how patients and families experience care at the end of life when a death occurs in a hospital. OBJECTIVES: (1) assess family member satisfaction with information sharing and communication, and (2) examine how satisfaction with information sharing and communication is associated with patient factors. METHODS: Using a cross-sectional survey, data were collected from family members of adult patients who died in an acute care organization. Correlation and factor analysis were conducted, and internal consistency assessed using Cronbach's alpha. Linear regression was performed to determine the relationship among patient variables and satisfaction on the Information Sharing and Communication (ISC) scale. RESULTS: There were 529 questionnaires available for analysis. Following correlation analysis and the dropping of redundant and conceptually irrelevant items, seven items remained for factor analysis. One factor was identified, described as information sharing and communication, that explained 76.3% of the variance. The questionnaire demonstrated good content and reliability (Cronbach's alpha 0.96). Overall, family members were satisfied with information sharing and communication (mean total satisfaction score 3.9, SD 1.1). The ISC total score was significantly associated with patient gender, the number of days in hospital before death, and the hospital program where the patient died. CONCLUSIONS: The ISC scale demonstrated good content validity and reliability. The ISC scale offers acute care organizations a means to assess the quality of information sharing and communication that transpires in care at the end of life.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".