Getting a Bigger Bang for Your Buck: A Collaborative Approach to Enhancing Dementia Education Planning in Long-Term Care Homes
Bibliographic record
Abstract
a collaborative of ontario-based long-term care associations, researchers, clinicians and educators representing various education initiatives related to dementia care and challenging behaviours used existing research evidence on adult learning principles, knowledge transfer and performance improvement to develop an evidence-based approach to support practice change and improvement in long-term care.the collaborative was led by the two provincial long-term care associations with no external funds to support its activities.this effort illustrates how people with common challenges, visions and goals can work together to share their intellectual and physical resources to address pervasive problems. Getting a Bigger Bang for Your Buck: A Collaborative Approach to EnhancingDementia Education Planning in Long-Term Care Homes supports available to facilitate decision-making, indicators for use, target audience and possible formats for the tool.• initial design for tool explored. april -June 2006 • initial version of tool developed.June 2006 • Meeting to review and refine the tool.group decides that current design is not sufficient and decides to use an algorithm to better help lTc homes to determine their needs for education and their capacity to support education.July 2006 • algorithm drafted.august 2006 • Meeting to review and refine the tool.initial discussion regarding the development of a matrix of education programs and marketing of the tool.september 2006 • Tool revised.october 2006• Meeting to review and further refine the tool and obtain consensus on revisions.Parts i and ii of the tool are finalized.• consultant support is terminated as members are able to undertake remaining tasks.november 2006 -February 2007 • Plans to pilot-test tool developed.• Members develop and refine the educational matrix (Part iii). March -april 2007• dena tool pilot-tested by lTc homes.• Feedback shared with collaborative group.changes to dena tool decided.
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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 | 0.051 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".