[P1–623]: WHY STANDARD CARE ENVIRONMENT ARCHITECTURE FAILS: CAUSING EXCESSIVE CHALLENGE TO QUALITY CARE, INCREASING BPSDS AND ACTIVELY OPPOSING WELLBEING—CAN EVIDENCE BASED, YET FUNDAMENTALLY NEW, APPROACHES TO CARE ENVIRONMENT DESIGN MAKE EVERYONE'S LIVES BETTER?
Bibliographic record
Abstract
Care homes continue closing at worrying rates and only 1% of adult social care remains rated as outstanding (CQC, The State of Health Care and Adult Social Care in England 2015/16). Is the fundamental architecture of dementia care environments playing a negative role in the delivery of outstanding care? If so, how? Can the underlying architecture, instead, play a wider positive role in making care giving easier, all round wellbeing better and the buildings more affordable to construct? Analysing the history of care environment design. Time spent giving art sessions in dementia care environments, and as a carer in general care environments for challenging behaviours. Study of research documents on enhancing wellbeing - dementia specific documents as well as general. Discussion with experts in dementia & neuroscience. Discussion with carers and people living with dementia. International Travelling Fellowship with the Winston Churchill Memorial Trust, to view acclaimed projects that support individuals living with a dementia and meeting experts across Australia, Canada & the Netherlands. Studying Cognitive Bias research, such as Hyperbolic Discounting, Functional Fixedness and Pseudo Certainty Effect. Looking at elements of relevant research from other fields of specialist care. Fundamental design models for elder & dementia care have not changed for decades. Rooted in a design family tree that stretches either from prison to asylum to hotel architecture, or from community to monastic to expensive ‘family style’ mini care homes. These standard models for dementia architecture do not take into account the fullness of modern research into wellbeing, instead focusing too narrowly on solving ‘symptoms of dementia’. Also the unique burden on staff delivering person centred care is not taken sufficiently into account. Excellent working examples of care can be found in part, but not in full. By bringing the various qualities together and combining them with up to date research on wellbeing in general as well as dementia specific, we believe it is possible to demonstrate new radical approaches to design for care. These architectural changes can make the giving of outstanding person centred care much more straight forward, enhancing wellbeing and reducing BPSDs.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".