Enhancing diversity to transform the future geriatric nursing workforce
Bibliographic record
Abstract
Objective: The growing number of older adults has placed unprecedented demands on the healthcare system while the number of geriatric nurses has not kept pace, and the gap is expected to widen. Simultaneously the older adult population is becoming more racially and ethnically diverse while the nursing workforce lacks diversity. Given the urgent need to increase and expand the number of diverse geriatric trained nurses, an innovative sustainable nursing program, Enhancing Diversity in Geriatric Nursing (EDGE) was implemented to train diverse nursing students in geriatrics thereby expanding the workforce and meeting the needs of underserved older adult populations.Methods: An Integrated Geriatric Training Program was implemented to provide didactic education through established online geriatric training modules, experiential learning through geriatric clinical placements, and telehealth training. EDGE supported students with stipends. Retention was enhanced via workshops and a combined mentorship program with peer tutoring to achieve a synergistic approach. Strategies were implemented to connect EDGE scholars to employment opportunities with underserved populations. Program impact was examined with a mixed-methods approach utilizing both quantitative and qualitative data to evaluate satisfaction and inform program refinement. Data were analyzed and Rapid Cycle Quality Improvement was utilized for monitoring and quality improvement.Results: EDGE resulted in the recruitment, enrollment, and geriatric training of 40 nursing students. EDGE scholars indicated program satisfaction.Conclusions: Disadvantaged nursing students benefitted from the EDGE Program. Longer term, the impact of the EDGE program has a strong likelihood for improving the care of underserved older adults by a diverse geriatric-trained nursing workforce.
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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 | 0.005 | 0.007 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".