Using a Serious Game as an Elicitation Tool in Interview Research: Reflections on Methodology
Bibliographic record
Abstract
It can be difficult to understand the process of making decisions in health care, because of both the complexity of health care systems and the demands faced by health care professionals. Serious games offer an underexplored opportunity to elicit data about decision-making. In this article, we present and reflect on a methodological case study where we used a serious game as part of a semistructured interview to study the process of decision-making. The game Resilience Challenge presented a patient's journey through a hospital, where a player had to make decisions that influenced patient care. The game was used during interviews with 20 nurses, both in person and remotely. Having a mini debrief with a participant after each game scenario provides to be a helpful technique to understand the participants' decision-making process and elicit tacit knowledge about their work. Serious games show promise as a methodological research tool to elicit the process of decision-making among health care professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".