Relational skill training for patient engagement and the creation of a trauma-informed critical care
Bibliographic record
Abstract
Patients and families in critical care have a high likelihood of previous and re-experienced trauma. Unaddressed, physical, and psychological impacts of these traumas can worsen outcomes for patients and families. A trauma-informed care approach has been proposed for critical care; however, training programs do not include relational competencies or de-escalation techniques, risking the re-traumatization of patients and families in critical care and negatively impacting clinicians. This article describes a strategy that can be adopted by critical care teams towards the creation of a trauma-informed critical care unit including the use of a framework for relational training. Principles of relationship management and de-escalation are discussed with the use of a fictional exemplar scenario. Key messages include a call to action for relational training for care teams to enhance skilled relational engagement of patients and families. This article also highlights the foundational importance of policies supporting a trauma-informed approach in critical care.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".