Death of a Simulated Pediatric Patient
Bibliographic record
Abstract
INTRODUCTION: A theoretical framework was recently proposed that encapsulates learner responses to simulated death due to action or inaction in the pediatric context. This framework, however, was developed at an institution that allows simulated death and thus does not address the experience of those centers at which this technique is not used. To address this, we performed a parallel qualitative study with the intent of augmenting the initial framework. METHODS: We conducted focus groups, using a constructivist grounded theory approach, using physicians and nurses who have experienced a simulated cardiac arrest. The participants were recruited via e-mail. Transcripts were analyzed by coders blinded to the original framework to generate a list of provisional themes that were iteratively refined. These themes were then compared with the themes from the original article and used to derive a consensus model that incorporated the most relevant features of each. RESULTS: Focus group data yielded 7 themes. Six were similar to those developed in the original framework. One important exception was noted; however, those learners not exposed to patient death due to action or inaction often felt that the mannequin's survival was artificial. This additional theme was incorporated into a revised framework. DISCUSSION: The original framework addresses most aspects of learner reactions to simulated death. Our work suggests that adding the theme pertaining to the lack of realism that can be perceived when the mannequin is unexpectedly saved results in a more robust theoretical framework transferable to centers that do not allow mannequin death.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".