ALL BEHAVIOUR HAS MEANING: CONCEPTUALIZING CHALLENGING ASSUMPTIONS IN DEMENTIA CARE
Bibliographic record
Abstract
“All behaviour has meaning” is one of the central tenants of dementia care research and education. Yet, the question, “What is behaviour?” is often left unasked within dementia care research, education, practice and policy. Drawing from perspectives in cultural gerontology, interpretive sociology and critical disability studies, this presentation theorizes ‘challenging’ assumptions about behaviour in discourses of dementia care. Our constructivist conceptual analysis of behaviour leads to a consideration of the behaviourist tradition in psychology, and the biopsychosocial model of dementia care and Thomas Kitwood’s and Carl Rogers’ seminal works on meaning, personhood and relationships. Within this tradition and these works, what behaviour means and why it matters is informed by ideas about adaptation and adjustment; concepts which posit behaviour as an expression (and measure) of the ‘person-environment fit’, and by virtue of that, a means of recognizing some people as out of place. Through unpacking the meaning of behaviour as a ‘challenging assumption’ in dementia care, we trace the relations between behaviour and personality. We contend that scholarly work of this nature within gerontology is critical to understanding the social significance of person-centred dementia care, and the challenges and opportunities a behaviourist approach poses for recognizing personhood in dementia.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.138 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".