The effectiveness of an educational project using older adult volunteers for training primary care nurse practitioners in geriatric assessment
Bibliographic record
Abstract
This article reports on the effectiveness of a pilot project, where older adult volunteers attending college campus programs were recruited to act as mock patients (MP) in a two-hour clinical simulation experience for primary care nurse practitioner (NP) students learning about geriatric assessment. Primary care providers, such as NPs, study variable content on geriatrics and see older adults in their primary care clinical practica yet report they desire more time in their training to practice geriatric assessment techniques, apply clinical practice recommendations, and discuss broader aspects of cases being managed by NPs within the interdisciplinary team. Utilization of live models acting as MPs with small groups of students acting as one provider is one way in which health care trainees can take more time to learn from each other as well as the models in the simulated clinical setting. The professor wrote a hypothetical case study based on clinical practice experience that either a male or female volunteer retiree could play as the MP. The case was a 75-year-old retiree with multiple other chronic conditions, on multiple medications, presenting with acute on chronic fatigue. Of the 48 students who participated, 47 returned surveys. Aggregate scores indicated an overall effectiveness of 88% across multiple aspects of geriatric primary care. Qualitative data indicated that the NP students would like more such cases in which they get more lead time with the case information to consider the myriad factors at play and have smaller groups of students per MP. The older adults who volunteered as MPs reported overwhelmingly that they found participating in the students’ education to be rewarding and a chance to offer input about improvement in the care of older adults in the current health system in our aging society. There was consistent feedback that the program should be continued and enhanced. The case content is offered in this article for use by other health care professionals who educate trainees in primary care.
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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 | 0.030 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".