Residents feel unprepared and unsupervised as leaders of cardiac arrest teams in teaching hospitals: A survey of internal medicine residents*
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine internal medicine residents' perceptions of the adequacy of their training to serve as in-hospital cardiac arrest team leaders, given the responsibility of managing acutely critically ill patients and with recent evidence suggesting that the quality of cardiopulmonary resuscitation provided in teaching hospitals is suboptimal. DESIGN: Cross-sectional postal survey. SETTING: Canadian internal medicine training programs. PARTICIPANTS: Internal medicine residents attending Canadian English-speaking medical schools. INTERVENTIONS: A survey was mailed to internal medicine residents asking questions relating to four domains: adequacy of training, perception of preparedness, adequacy of supervision and feedback, and effectiveness of additional training tools. MEASUREMENTS AND MAIN RESULTS: Of the 654 residents who were sent the survey, 289 residents (44.2%) responded. Almost half of the respondents (49.3%) felt inadequately trained to lead cardiac arrest teams. Many (50.9%) felt that the advanced cardiac life support course did not provide the necessary training for team leadership. A substantial number of respondents (40%) reported receiving no additional cardiac arrest training beyond the advanced cardiac life support course. Only 52.1% of respondents felt prepared to lead a cardiac arrest team, with 55.3% worrying that they made errors. Few respondents reported receiving supervision during weekdays (14.2%) or evenings and weekends (1.4%). Very few respondents reported receiving postevent debriefing (5.9%) or any performance feedback (1.3%). Level of training and receiving performance feedback were associated with perception of adequacy of training (r(2) = .085, p < .001). Respondents felt that additional training involving full-scale simulation, leadership skills training, and postevent debriefing would be most effective in increasing their skills and confidence. CONCLUSIONS: The results suggest that residents perceive deficits in their training and supervision to care for critically ill patients as cardiac arrest team leaders. This raises sufficient concern to prompt teaching hospitals and medical schools to consider including more appropriate supervision, feedback, and further education for residents in their role as cardiac arrest team leaders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".