Barriers and Willingness to Undertake Cardiopulmonary Resuscitation Reported by Medical Students Dependent on Their Place of Residence—A Single-Center Study
Bibliographic record
Abstract
Background: Bystander-administered cardiopulmonary resuscitation (CPR) is crucial for the survival of out-of-hospital cardiac arrests. However, only roughly 58% of bystanders would provide CPR, with wide variations across different regions. Identifying each factor affecting the barrier or readiness to perform resuscitation is a significant challenge for researchers. This study aimed to evaluate the obstacles preventing first-year medical students from initiating CPR, focusing on the size of domestic residential environments and the time and methodology of practical training. Methods: The original online questionnaire surveyed first-year medical students at the Medical University of Łódź from February 1 to March 2, 2024. The questionnaire development involved a literature review, expert evaluation, and pilot testing. Participation was voluntary and anonymous, with strict inclusion and exclusion criteria. The data were analyzed using PQStat software, employing descriptive statistics. Results: The study revealed that 271 medical students reported a similar median of barriers regardless of the place of residence (median (Me) = 5, interquartile range (IQR) 2–6.25 vs. Me = 4, IQR 3–6 vs. Me = 4, IQR 3–6, p = 0.620). Out of 18 analyzed barriers, the only significant difference was found for crowded places. Medical students living in cities most frequently reported a willingness to perform CPR with rescue breaths for all victims. Those who grew up in towns <100,000 residents were less willing to start CPR if an unknown adult were the victim (rural area: 39.2% vs. town: 17.6% vs. city: 45.1%, p < 0.01). The number of reported barriers was similar regardless of training type and the time since training; however, the nature of these barriers varied after a year passed. Conclusions: Respondents across various locations reported similar number and types of barriers to performing CPR, including the most commonly declared fear of harm, uncertainty about recognizing cardiac arrest, and concerns about disease transmission. Although differences connected to the type of victims were observed, its low or moderate practical significance needs more comprehensive research on the impact of the size of the place of residence on the willingness to perform resuscitation and the related barriers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".