Delivering Flexible Education and Training to Health Professionals: Caring for Older Adults in Disasters
Bibliographic record
Abstract
The National Center for Disaster Medicine and Public Health (NCDMPH), in collaboration with over 20 subject matter experts, created a competency-based curriculum titled Caring for Older Adults in Disasters: A Curriculum for Health Professionals. Educators and trainers of health professionals are the target audience for this curriculum. The curriculum was designed to provide breadth of content yet flexibility for trainers to tailor lessons, or select particular lessons, for the needs of their learners and organizations. The curriculum covers conditions present in the older adult population that may affect their disaster preparedness, response, and recovery; issues related to specific types of disasters; considerations for the care of older adults throughout the disaster cycle; topics related to specific settings in which older adults receive care; and ethical and legal considerations. An excerpt of the final capstone lesson is included. These capstone activities can be used in conjunction with the curriculum or as part of stand-alone preparedness training. This article describes the development process, elements of each lesson, the content covered, and options for use of the curriculum in education and training for health professionals. The curriculum is freely available online at the NCDMPH website at http://ncdmph.usuhs.edu (Disaster Med Public Health Preparedness. 2016;10:633-637).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".