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Record W3085450991 · doi:10.1177/0733464820955089

Process Models to Understand Resident-to-Resident Aggression Among Residents With Dementia in Long-Term Care

2020· article· en· W3085450991 on OpenAlexaffabout
David Burnes, Manaal Syed, Jessica Hsieh

Bibliographic record

VenueJournal of Applied Gerontology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDementiaAggressionConceptualizationLong-term carePsychologyFocus groupPsychological interventionInterpersonal communicationCognitionQualitative researchGerontologyMedicineDevelopmental psychologyPsychiatrySocial psychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVES: Resident-to-resident aggression (RRA) is a prevalent form of interpersonal violence in long-term care (LTC) settings. Research to guide preventive interventions is limited. Using social-ecological and need-driven dementia-compromised behavior perspectives, we sought to generate process models representing common RRA pathways in dementia-specific LTC units. RESEARCH METHODS: = 36) exposed to everyday resident interactions at two urban LTC facilities in Toronto, Canada. Semistructured interviews were audio-recorded and transcribed. Two independent raters coded the transcripts using iterative, constant comparison analytic processes. RESULTS: Two distinct RRA process models in dementia-specific LTC units were developed. Models reflect sequential pathways driven by residents' benign or responsive behaviors and cognitive processing limitations, with escalation points within resident dyads or groups. IMPLICATIONS: This study furthers RRA conceptualization as a process rather than an aggressive event. Models capture unique RRA manifestations in dementia-specific LTC units and entrypoints for prevention or management.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.048
GPT teacher head0.331
Teacher spread0.283 · 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 designQualitative
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

Citations8
Published2020
Admission routes2
Has abstractyes

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