Perspectives of teamwork : Looking at teamwork leadership through the lens of student Emergency Medical Technicians in the State of Qatar
Bibliographic record
Abstract
Paramedics play a crucial role in the medical community and society as a whole. To become a paramedic, learners must acquire knowledge of emergency medicine, but also procedures and skills in time management, communication skills and teamwork. Learning these goals can be daunting. Part of the challenge is becoming proficient in successfully managing medical emergencies in a high-fidelity simulated learning environment which leads to one’s shortcomings being exposed. These simulation-based learning environments are designed for students to hone the technical and also communication skills taught in the classroom. Other challenges relate to their interactions with the high-fidelity simulated patient. This study explores the interactions between team members in treating a heavily pregnant female Muslim high-fidelity simulated patient with multi-trauma injuries and the interactions between the patient and the team members. This investigation took place at an emergency medical technician diploma program at a Canadian technical college in the State of Qatar. Participants are second year students in the simulation lab course who were tasked with treating, stabilising and transporting a multi-trauma patient. Interpretative phenomenological analysis and activity theory are used to bring depth to the analysis of the data from semi-structure interviews and observations. The results of this work revealed a number of findings. All student participants had received the same information on how a paramedic team should function in a medical emergency. However, their personal views of how a well-functioning team should work were different from that of their colleagues and of the program. Thus, conflict in critical care management transpired. Another finding revealed that the student participants actions and language showed their view of the mannequin fluctuated between seeing it as an artefact to an actual patient. Also revealed was how religion influences the decision-making process, ultimately, leading to hesitation in treatment. These findings raise important questions on how to further maximise learning opportunities in simulation-based medical training environments.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".