Implementing Montessori Methods for Dementia: A Scoping Review
Bibliographic record
Abstract
Purpose of the Study: A scoping review was conducted to develop an understanding of Montessori-based programing (MBP) approaches used in dementia care and to identify optimal ways to implement these programs across various settings. Design and Methods: Six peer-reviewed databases were searched for relevant abstracts by 2 independent reviewers. Included articles and book chapters were those available in English and published by the end of January 2016. Twenty-three articles and 2 book chapters met the inclusion criteria. Results: Four approaches to implementing MBP were identified: (a) staff assisted (n = 14); (b) intergenerational (n = 5); (c) resident assisted (n = 4); and (d) volunteer or family assisted (n = 2). There is a high degree of variability with how MBP was delivered and no clearly established "best practices" or standardized protocol emerged across approaches except for resident-assisted MBP. Implications: The findings from this scoping review provide an initial road map on suggestions for implementing MBP across dementia care settings. Irrespective of implementation approach, there are several pragmatic and logistical issues that need to be taken into account for optimal implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".