A long-range perspective: A qualitative evaluation of simulation training for contingency operations among interprofessional behavioral health officers over time
Bibliographic record
Abstract
Large-Scale Combat Operations (LSCO) will necessitate behavioral health professionals who can deploy interventions that can be applied by non-behavioral health professionals in a prolonged field setting, representing a fundamental shift from the service delivery in garrison. Unfortunately, this means there will be little opportunity for behavioral health professionals to gain experience prior to implementation, which can risk mission failure due to inadequate preparation. Simulation education and exercise training are hallmarks of both military and healthcare training but have been underutilized in behavioral health domains. The current study presents the results of a qualitative evaluation of a novel simulation-based training exercise for behavioral health training in a military field setting. Graduates of an interprofessional military behavioral health training program were contacted approximately 4-9 years after their engagement in this training and asked to reflect on how this training experience influenced their readiness for behavioral health care in deployed settings. Results indicated that a simulation-based training methodology can faithfully capture some of the key facets of behavioral health intervention in austere and/or deployed settings - with numerous respondents indicating fidelity in comparison to relevant real-world scenarios subsequently faced at various points following graduation. Both positive and critical feedback from participants are discussed regarding the potential further development of simulation-based training programs, as well as the necessary scalability in the context of future LSCOs.
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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 | 0.045 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".