Responsive Behaviours in Dementia: Developing and Implementing the Behavioural Supports Ontario Initiative
Bibliographic record
Abstract
A policy initiative known as Behavioural Supports Ontario (BSO) was developed and implemented in Ontario between 2009 and 2013. This thesis seeks to identify the factors that led to BSOâ s development and explore the factors that may have influenced its implementation. The thesis used case study methodology. Thematic analysis of semi-structured interview and document data sources identified the following factors. Overall, the factors leading to the development of the BSO initiative included: (1) an increasing awareness of the negative effects of inappropriate care; (2) the ineffective use of emergency departments; (3) high numbers of alternate level of care (ALC) days; (4) uncoordinated care across care provider organizations; and (5) ongoing staffing issues related to lack of time and training on how to provide appropriate care to people with problematic (responsive) behaviours. The factors that influenced the implementation of the BSO initiative included an expansion of funds available for the long-term care sector specifically, a growing sense of implementation fatigue among service providers, the effect of knowledge discontinuity when BSO trained staff left their positions, and the risk that funds and human resources earmarked for the BSO initiative could be reallocated by service providers responding to immediate staffing needs. The findings of this thesis support the theoretical concepts related to causal stories in agenda-setting and the policy development and implementation process.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".