MONTESSORI METHODS FOR DEMENTIA™ IN ONTARIO LONG-TERM CARE HOMES: STAFF PERCEPTIONS OF FACTORS AFFECTING IMPLEMENTATION
Bibliographic record
Abstract
Objectives: Research shows that Montessori-based activities can help address responsive behaviours experienced by persons with dementia by increasing their participation in and enjoyment of daily life. The purpose of this study was to investigate staff perceptions of factors that affect the implementation of Montessori Methods for Dementia™ (MMD) in Ontario long-term care (LTC) homes. Methods: Qualitative data was obtained during semi-structured telephone interviews with 17 participants who were putting MMD into practice in Ontario LTC homes. The study was guided by a political economy of aging perspective using thematic analysis to elucidate the various factors that affected the implementation of MMD. Results: Several themes emerged from the data: Regulating and Funding Medical Practices; Shifting Practice Amidst Resistance to Change; Educating and Understanding; Seeing Results is Believing; Being Supported; (Re-)Connecting People and Passions; and Improving Residents’ Quality of Life. Barriers such as insufficient funding and negative attitudes toward activities and MMD reinforced a task-oriented biomedical model of care, whereas various forms of support and understanding helped put MMD into practice as a person-centred program, which improved the quality of life of residents with dementia, staff and family members. Conclusions: The results from this research can help ensure that MMD are as practical and easy to implement as possible despite perceived barriers so that persons with dementia in LTC and their partners in care can have a good quality of life. The findings include suggestions for future research, reducing staff hierarchies and ensuring there is sufficient organizational, financial, educational, and personal support.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".