Clinician’s Corner: Meeting family care needs during resuscitative procedures and cardiac arrest in the emergency department
Bibliographic record
Abstract
Emergency nurses in Canada provide care to many thousands of critically ill and injured patients, and their families, each year (Rowe et al., 2020). Unfortunately, some Emergency Department (ED) patients and families report a lack of psychosocial and emotional caring (Gordon et al., 2010). Many resuscitative processes and procedures have been described as dehumanizing and traumatic for families (De Stefano et al., 2016; Jang & Choe, 2019). Significant negative emotional and physiological impacts may remain after hospitalization for the patient and their loved ones, whether receiving care for medical, (Davidson & Harvey, 2016) trauma, (McGahey-Oakland et al., 2007) or cardiac arrest presentations (Leske et al., 2013). Families of patients who survive (and those who do not survive) have reported persistent negative psychological effects weeks and months after receiving care in the ED (Jang & Choe, 2019; Keyes et al., 2014; Sawyer et al., 2020).
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".