1387: PREPARING PARAMEDICS TO SUPPORT AND COMMUNICATE WITH FAMILIES DURING AND AFTER RESUSCITATION
Bibliographic record
Abstract
Introduction: First responders frequently enter patients’ homes and encounter families during resuscitation for cardiac arrest. Data around preparation and pathways for Family Presence During Resuscitation (FPDR) and the Family Facilitator role are rapidly evolving for inpatient care settings yet there is little education available for paramedics to partner with families during emergencies. Our aim was to assess paramedics’ understanding of family needs during crisis and develop training for FPDR using a transdisciplinary, competency-based educational approach. Methods: A multidisciplinary group including nurses, physicians, paramedics, and chaplains developed a virtual workshop to deploy for 100 paramedics in a large Canadian province. Educational needs were identified and content was customized for paramedics using available evidence on family needs during resuscitation and established competencies for inpatient Family Facilitators (chaplains) who respond to cardiac arrest events. Results: Paramedics identified a large gap in the care provided to the families of cardiac arrest victims in out of hospital cardiac arrest. Whereas patients and families treated within a healthcare facility will typically have access to chaplain services, those receiving care outside of these centers may receive little or no support. The learning objectives identified were to (1) Understand patient- and family-centered cardiac arrest and what is known about FPDR, (2) recognize the needs of families experiencing cardiac arrest care, (3) prepare to be emotionally present for families during a call to cardiac arrest, (4) utilize the ABCD tool to engage with families during and after cardiac arrest, and (5) recognize the process and benefits of real-time reflection and debriefing. The multidisciplinary group then developed an ‘ABCD’ tool to provide cognitive cues for the steps of family communication during cardiac arrest. Conclusions: Competency-based education addressing family care during resuscitation is essential for first responders. This novel program and collaborative effort is one of the first to prepare emergency responders to support and communicate with families during cardiac arrest care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".