Reimagining neuroscientific and andragogical principles for dementia care education
Bibliographic record
Abstract
This article aims to explore the integration of Louis Cozolino's (2013) andragogical strategies with the tenets of person-centered dementia care practices to enhance dementia care education. The article examines the multiple dimensions of learning in adulthood, highlighting the role of neural plasticity and lifelong brain adaptation in shaping learning and experiential strategies. This in-depth evaluation underscores the significance of tailoring andragogical approaches to the needs of adult learners, who, in this context, are care providers for persons with dementia. This is done through proper understanding of the neurobiological realities and the unique learning needs of adults. Such tailored approaches can be aligned with the brain's adaptive nature by recognizing the intricate interplay of cognitive, emotional, and social dimensions. Highlighting the need for including lessons on the person-centered approach in dementia care education, the paper argues that adult learners - who are essentially part of the dementia care workforce - first need to learn, appreciate, and embrace the approach before applying it in their caregiving practices. This article presents an overarching argument that integration of Cozolino's principles of adult learning with tenets of person-centered dementia care could provide a robust framework for dementia care education.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".