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Record W4255657922 · doi:10.1093/geront/gnv471.18

FORMATIVE EVALUATION IN THE IMPLEMENTATION OF THE HOME SAFETY TOOLKIT FOR VETERANS WITH DEMENTIA

2015· article· en· W4255657922 on OpenAlexaff
I Kandil, L García, Lynn McCleary, Neil Drummond, Katherine S. McGilton, Jean Triscott, Scott A. Trudeau, Kathy J. Horvath, P Trudeau, Laurel E. Radwin, Eleanor S. McConnell

Bibliographic record

VenueThe Gerontologist · 2015
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsToronto Rehabilitation InstituteUniversity of AlbertaBrock UniversityUniversity of OttawaBruyère
Fundersnot available
KeywordsFormative assessmentDementiaComputer scienceMedical educationPsychologyGerontologyMedicinePedagogy

Abstract

fetched live from OpenAlex

Objective: To identify the challenges faced by CGs prior, during, and following the transition to LTC that address access to services. Methods: Caregiver -person with dementia (PWD) dyads were identified from a larger prospective study that occurred over a period of 18 months. Semistructured interviews were designed to appraise the CG's situation over time. A total of 25 participants initiated the process of relocating to LTC, and 7 were placed in a LTC facility by the end of study. Interview data was thematically organized, coded, and categorized. Results: Participants' experiences were grouped under stages in the timeline of relocation to LTC. Initially, relocation was motivated by increasing CG burden and an awareness of the PWD's health decline. As the dyads advanced in the trajectory, the decision making process was complicated by challenges in predicting the dementia trajectory and a marked concern about waiting lists. Finally, post-move, and despite relocation, CGs continued to monitor PWD's care and health decline. The results are discussed within the context of strategies for facilitating transitions that address systemic barriers and access to information, thereby adding to the already rich literature on the emotional adaptation to relocation.

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 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)0.006
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.137
GPT teacher head0.429
Teacher spread0.292 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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