Gerontology competencies: Construction, consensus and contribution
Bibliographic record
Abstract
The Academy for Gerontology in Higher Education (AGHE) in 2014 approved the first integrative "Gerontology Competencies for Undergraduate and Graduate Education"©. This article describes the background, thought development, guiding framework and consensus process for its construction. A modified Delphi method utilizing seven review rounds within three developmental cycles, with gerontology educators from 30 institutions, achieved input and consensus. The comprehensive framework has ten major domains, employs three categories each including multiple selective competencies. Six Category I competencies are essential orientations to gerontology. Four Category II competencies are "interactional" processes of knowing and doing across the field. Category III provides eight selective competencies for sectors where gerontologists may work. From educators' feedback, gerontology characteristics emerged: multi-system approaches; interdisciplinary; communication of older adults' "voices" and strengths; research utilization. The discussion includes the contribution of competency-based gerontology to students and aging workforce development as well as next steps, outcome measurement, levelling and accreditation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.171 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".