Bridging the Workforce Gap for Our Aging Society: How to Increase and Improve Knowledge and Training. Report of an Expert Panel
Bibliographic record
Abstract
The healthcare workforce is currently unprepared for the increasing number of older persons and the complexities of their healthcare needs. Too few healthcare workers are adequately trained in geriatrics, and developers of educational curricula across healthcare disciplines have been slow to incorporate or require geriatric training. In April 2003, leaders in geriatrics met in Washington, D.C., to discuss and recommend solutions to the growing shortage of an appropriately trained workforce for geriatric research, education, and patient care. After considering data, presenting statistics, and offering insights into the future, the conference concluded by formulating recommendations to meet specific challenges. This report is a summary of the conference proceedings and recommendations, and it serves as a reminder that demographic trends and an everexpanding geriatric knowledge base demand not only attention, but also action.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.024 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".