Characterization of the term "wandering" in dementia research: A scoping review
Bibliographic record
Abstract
Background: Dementia is an important public health concern, affecting approximately 50 million people world- wide. The term "wandering" is generally associated with negative behaviours among persons with dementia; however, physical movements of wandering can benefit persons with dementia. Despite its common usage, the term wandering does not have a standardized definition. The aim of this study was to examine definitions of the term wandering in academic literature as it pertains to persons living with dementia and how they compare to one another. Methods: This scoping review of the academic literature used Arksey and O’Malley’s (2007) framework. Papers were identified through a search of seven electronic databases. Papers were included from any year and study design provided they were in English, French, or Spanish and used terms related to “dementia”, wandering”, or “terminology.” Four members of the team participated in screening, reviewing titles, abstracts, and full-text articles. Data was extracted and recorded in an Excel spreadsheet, and a fifth researcher reviewed the extracted data to confirm validity of the extraction. Results: The search identified 106 academic papers, predominantly originating from Great Britain (50%) and the United States of America (45.28%) The term “wandering” began to appear in publications in 1990 and has been increasing since then with different definitions, some even contradictory. The definitions fell into seven domains: aimless walking, normal routine/walking for pleasure, elopement, critical wandering, disorientation in spatial navigation, combination, and other. Conclusion: This scoping review identified that the term “wandering” remains unclear. Definitions across studies are contradictory, vague, and have both negative and positive connotations. With a demographic shift towards older populations and an increasing prevalence of dementia, it is critical to understand the importance of consistent terminology to potentially avoid further confusion, misinterpretation, and stigmatization.
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 | 0.050 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.047 | 0.039 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".