Bibliographic record
Abstract
This thesis explores how innovative behavioural and virtual environment simulations could benefit healthcare and health policy. In the first half of the thesis I review the use of behavioural simulations in healthcare and contextualise an evidence-based approach for development and analysis. This approach is informs the successful design and completion of two simulations – The Crucible and Lateral Play. The Crucible was designed to improve leadership skills and understanding of the Health and Social Care Act amongst clinicians. Lateral Play was designed to aid organisational development of Imperial College Health Partners, the Academic Health Sciences Partnership in North-West London. Detailed analysis demonstrated, for the first time, the measurable positive effect of Behavioural Simulations on participant learning and behaviours. In the second half of the thesis I examine and demonstrate the potential for virtual world simulations to enhance major incident preparation, reviewing the evidence behind major incident training the potential benefits of using virtual world environments via a user-needs analysis and expert advisory group. I describe the successful design, development and assessment of three virtual world scenarios for multidisciplinary major incident training in the context of a bomb blast. Face and content validity is demonstrated and performance assessed in both technical and non-technical skills. Finally, I determine the feasibility of utilising a virtual trauma scenario for long-distance training between the UK, Canada and Southern Africa. The thesis concludes with an overall discussion of the pertinent findings, limitations and implications for future practice and research.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".