Family presence during cardiopulmonary resuscitation: cardiac health care professionals' perspectives.
Bibliographic record
Abstract
BACKGROUND: Family presence (FP) during cardiopulmonary resuscitation (CPR) is becoming an increasing practice. Within current literature, the attitudes and beliefs towards FP of cardiac health care professionals in Canada are limited. PURPOSE: The purpose of this project was to examine the perceptions of cardiac health care professionals (n=368) concerning FP during CPR. METHOD: A survey was conducted to explore the attitudes and beliefs of cardiac health care professionals towards family presence during CPR within five Edmonton and surrounding area hospitals. RESULTS: The response rate was 46%, with the greatest response from nurses and physicians. Of the respondents, 44.3% believed that family should have the option to be present, and 40.9% believed that family should be allowed at the bedside during CPR. Less than half of the respondents had experience with FP during CPR. The barriers identified towards FP were lack of support for families, the experience would be too traumatic for families, families would not understand the procedures, fear of families physically interfering with procedures, FP would increase stress levels among staff, and tradition and politics excludes FP. CONCLUSION: Despite less than half the respondents supporting FP the majority endorsed development of policy and procedures to overcome barriers to FP during CPR.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".