Operationalising elaboration theory for simulation instruction design: a Delphi study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the feasibility of incorporating the Delphi process within the simplifying conditions method (SCM) described in elaboration theory (ET) to identify conditions impacting the complexity of procedural skills for novice learners. METHODS: We generated an initial list of conditions impacting the complexity of lumbar puncture (LP) from key informant interviews (n = 5) and a literature review. Eighteen clinician-educators from six different medical specialties were subsequently recruited as expert panellists. Over three Delphi rounds, these panellists rated: (i) their agreement with the inclusion of the simple version of the conditions in a representative ('epitome') training scenario, and (ii) how much the inverse (complex) version increases LP complexity for a novice. Cronbach's α-values were used to assess inter-rater agreement. RESULTS: All panellists completed Rounds 1 and 2 of the survey and 17 completed Round 3. In Round 1, Cronbach's α-values were 0.89 and 0.94 for conditions that simplify and increase LP complexity, respectively; both values increased to 0.98 in Rounds 2 and 3. With the exception of 'high CSF (cerebral spinal fluid) pressure', panellists agreed with the inclusion of all conditions in the simplest (epitome) training scenario. Panellists rated patient movement, spinal anatomy, patient cooperativeness, body habitus, and the presence or absence of an experienced assistant as having the greatest impact on the complexity of LP. CONCLUSIONS: This study demonstrated the feasibility of using expert consensus to establish conditions impacting the complexity of procedural skills, and the benefits of incorporating the Delphi method into the SCM. These data can be used to develop and sequence simulation scenarios in a progressively challenging manner. If the theorised learning gains associated with ET are realised, the methods described in this study may be applied to the design of simulation training for other procedural and non-procedural skills, thereby advancing the agenda of theoretically based instruction design in health care simulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".