Supporting rural families during interhospital patient transfers for critical illness events: An exploration of an acceptable communication process
Bibliographic record
Abstract
Critically ill patients in rural areas at times require an interhospital transfer from their local hospital to an urban tertiary care centre for advanced critical care services not available locally. Family members have described this transfer window as a communication blackout and one of the most stressful times of their relative's critical illness event. OBJECTIVE: To explore what communication process would be most acceptable between family members and transfer team members (consisting of critical care nurses, paramedics, and physicians) during interhospital transfers of critically ill patients. RESEARCH METHODOLOGY: Using a qualitative descriptive approach of critical thematic analysis, data were collected in September and November 2022, from focus groups of five family members and four transfer team members who experienced this phenomenon. SETTING: Rural Canada where speciality services such as interventional cardiology and neurosurgery are unavailable, and a tertiary care hospital is more than 160 km away. FINDINGS: Within themes of unequal power relations and status-based hierarchies, family members described how communication during interhospital transfers supports connection and coping, challenges experienced in accessing information, an overwhelming unknown, and practical challenges of the transfer. Transfer team members described a context of power relations and status-based hierarchies in which themes of transfer team burden, role confusion or connection, protection and management of family members, and complexities of information sharing during interhospital transfers were identified. CONCLUSION: In critical illness, communication linkages are created between healthcare providers and family members but are broken during an interhospital transfer resulting in increased stress for family members. Acceptable communication elements described by transfer team members and family members may maintain these linkages during the transfer window. IMPLICATIONS FOR CLINICAL PRACTICE: These findings provide the foundation for critical care nurses and their professional colleagues to take family care to the next level with an explicit communication strategy during interhospital transfers.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".