LEVELING AND ADAPTING GERONTOLOGY COMPETENCIES: EXAMPLES FROM CANADA AND THE U.S.
Bibliographic record
Abstract
Background: AGHE recently published Gerontology Competencies for Undergraduate and Graduate Education (2014) that represents the most comprehensive effort to date to identify gerontology competencies. The challenge for educators is to integrate competencies into gerontological curricula and to use them in measuring the skills of gerontology program graduates. This presentation reports on two such attempts, with particular emphasis on “leveling” competencies. Developing an Introductory Course in Canada: A group of faculty affiliated with the Council of Ontario Universities is leading the development of an introductory gerontology course in seven modules based on the AGHE foundational competencies. Since it is anticipated that the course would be useful both for students and practitioners, the developers share their perspectives on adapting the competencies for various levels. Measuring the Skills of Gerontology Program Graduates in West Virginia: The National Association for Professional Gerontologists (NAPG), in cooperation with the Gerontology Master of Arts Program at San Francisco State University, measured gerontology competencies by operationalizing 18 skill outcomes. Forty-nine master’s students wrote 400 essay answers that were tested for inter-rater agreement using one-way analysis of variance. The fact that there was no significant difference among three raters (f = .28, p = .76) in scoring essay responses indicates reliability. This method of measuring competencies developed with master’s students has been used as an exit exam for graduates of the Gerontology Associate Arts Program at Bridge Valley Community and Technical College in South Charleston, West Virginia. Researchers discuss the adaptation of competency testing at these two educational levels.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".