The “PRIMING, TIMING, MIMING” Model of Individualized Behavioural Care Planning for Residents with Dementia
Bibliographic record
Abstract
The current paper introduces the “Priming/Timing/Miming” Model of Behavioural Care Planning for persons with Dementia. This simple heuristic provides a quick, easy and systematic way to select from the vast number of behavioural strategies offered in the BPSD literature and to organize these in a way that can be incorporated into an individualized Behaviour Care Plan to deliver personal care to persons with dementia and also to develop a larger plan of care. An entire care plan is captured on one double-sided sheet of paper that can be updated by simply highlighting the relevant sections at team meetings. It has also been accommodated into electronic charting. An example using the “Priming/Timing/Miming” Care Plan document is provided. The model and document were developed with Multi-disciplinary input over more than a decade, on several inpatient dementia units. Training to use the model and care plan has been provided to numerous groups including Long-Term Care facilities, Retirement Homes, Dementia Units, Acute Care Hospital Units, Inpatient Acquired Brain Injury Staff and Outreach Teams. The model and care plan document are robust, successfully accommodating a wide range of behavioural presentations.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".