PERSPECTIVES ON THE RISK FACTORS ASSOCIATED WITH MISSING INCIDENTS IN PERSONS LIVING WITH DEMENTIA
Bibliographic record
Abstract
Abstract Persons living with dementia are at higher risk of getting lost and going missing. The adverse outcomes of missing incidents are stressful for persons living with dementia and those who care for them. This study aimed to identify and describe the perspectives of persons with dementia, caregivers and community support organizations on risk factors. Generic qualitative description informed our methods. We conducted 30 virtual interviews with persons who live with dementia, professional and family caregivers and community support organization representatives. We used a card sort to elicit and describe perspectives on the importance of 27 risk factors commonly associated with missing incidents in persons living with dementia. Interviews were digitally recorded, transcribed verbatim, and subjected to content analysis to determine the presence of relevant words, themes, and concepts. Participants reported multiple experiences of a person going missing, impressions, and suggested relationships between factors such as environmental contexts. The most critical risk factors associated with getting lost and going missing were cognitive impairment, unmet needs, and inadequate concentration of services and resources. In contrast, race, education, and gender were perceived as unimportant pertaining to risk factors related to missing incidents in persons living with dementia. An understanding of the perceived importance of risks associated with missing incidents enhances a person-centered approach to addressing unmet needs, services and resources that balances quality of life with maintaining safety.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".