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Record W3159977488 · doi:10.1155/2021/5572764

Strategies to Locate Lost Persons with Dementia: A Case Study of Ontario First Responders

2021· article· en· W3159977488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Aging Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of AlbertaUniversity of Waterloo
FundersMitacsAlzheimer Society
KeywordsDementiaGeneral partnershipMedicineGuidelineMedical educationPublic relationsGerontologyDiseasePolitical science

Abstract

fetched live from OpenAlex

Information on strategies and practices in the search of missing persons with dementia is inconsistent which creates challenges for first responders, such as police, when they choose appropriate search and rescue approaches. The purpose of this study was to describe current strategies among police services in Ontario. Telephone interviews with police were conducted. Questions included what strategies were used for locating missing persons living with dementia, and what gaps exist in search practices. Participants described they used high- and low-tech solutions in search and rescue. They identified gaps in education and awareness, proactive strategies, resources, and funding. Information collected from the interviews was used to develop a practice guideline for police in partnership with the Alzheimer Society of Ontario.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.218
GPT teacher head0.504
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it