Solutions to the conundrum: Implementing adult-gerontology graduate programs with current resources
Bibliographic record
Abstract
Implementation of the Consensus Model has created new certifications and advanced practice nursing education programs. The combining or creation of new adult-gerontology programs prepares Advanced Practice Registered Nurses ( APRNs ) to care for the adult-gerontology population across the health spectrum of wellness to illness. As often occurs with legal or regulatory change, conundrums can develop for graduate nursing education programs during the implementation efforts as they struggle to meet the new requirements before needed resources are available. One of the most challenging problems has been developing the Adult-Gerontology population foci with available current resources. In order to address the conundrum, strategies need to be developed which will enhance graduate nursing education programs, create partnerships with practice and create service learning opportunities. The change of preparing adult-gerontology APRNs capable of delivering care across the spectrum of wellness to illness in a variety of health care settings will eventually help address the needs of society, but there are barriers to the initial implementation which have to be overcome in order to achieve a successful long term solution to the nation’s health care requirements. Formative strategies to effectively deal with the conundrums will keep the process of change moving forward. Working together collaboratively in a team approach will enable academia, practice, and the community to identify and bridge the hurdles to achieve success.
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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 | 0.077 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 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".