Comparison of simulation debriefs with traditional needs assessment methods: a qualitative exploratory study in a critical care community setting
Bibliographic record
Abstract
OBJECTIVE: To better understand the potential of a needs assessment approach using qualitative data from manikin-based and virtual patient simulation debriefing sessions compared with traditional data collection methods (ie, focus groups and interviews). DESIGN: Original data from simulation debrief sessions was compared and contrasted with data from an earlier assessment of critical care needs in a community setting (using focus groups and interviews), thus undertaking secondary analysis of data. Time and cost data were also examined. Debrief sessions were coded using deductive and inductive techniques. Matrices were used to explore the commonalities, differences and emergent findings across the methods. SETTING: Critical care unit in a community hospital setting. RESULTS: Interviews and focus groups yielded 684 and 647 min of audio-recordings, respectively. The manikin-based debrief recordings averaged 22 min (total=130 min) and virtual patient debrief recordings averaged 31 min (total=186 min). The approximate cost for the interviews and focus groups was $13 560, for manikin-based simulation debriefs was $4030 and for the virtual patient debriefs was $3475. Fifteen of 20 total themes were common across the simulation debriefs and interview/focus group data. Simulation-specific themes were identified, including fidelity (environment, equipment and psychological) and the multiple roles of the simulation instructor (educative, promoting reflection and assessing needs). CONCLUSIONS: Given current fiscal realities, the dual benefit of being educative and identifying needs is appealing. While simulation is an innovative method to conduct needs assessments, it is important to recognise that there are trade-offs with the selection of methods.
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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 | 0.074 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".