Assessment Tools for Baseline and Follow-up Measurement of the Dementia-Friendliness of a Community: Scoping review protocol
Bibliographic record
Abstract
Objective: In this review we aim to identify and appraise assessment tools that have been used or developed to quantitatively assess the dementia-friendliness of communities. Introduction: An assessment of the dementia-friendliness of a community can establish indicators to measure progress and can support an understanding of how dementia-friendly community initiatives impact the experience of living with dementia. However, because of a lack of available evaluation tools, assessments of the dementia-friendliness of a community are not always undertaken, making it difficult to evaluate the impact of community action plans. Inclusion criteria: In this scoping review we will consider published and unpublished sources on tools that quantitatively assess the dementia-friendliness of a community. Studies that have investigated dementia-friendly environments in healthcare organizations, such as hospitals, will not be considered in this review. Methods: The JBI methodology for scoping reviews will be followed. Medline, CINAHIL, PsychInfo, EMBase, EMCare, HealthStar and AgeLine will be searched from inception date for relevant articles. Unpublished sources will be identified using Google advanced search functions and snowball sampling of representatives from communities that have conducted dementia-friendly assessments in Canada, Australia, New Zealand, the United States, and the United Kingdom. To investigate how the tools have been used in research and in practice related to DFCs, we will scan all the “cited by” articles of sources found. Tables and a narrative summary will be used to describe and assess the scope and features of the assessment tools and outline their contribution to research and practice.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.002 |
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; both teacher heads agree on what is shown here.
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".