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Record W4285394528 · doi:10.28984/npoj.v2i1.397

Exploring Behaviours Related to Pain Indication for Residents in Long-Term Care with Dementia

2022· article· en· W4285394528 on OpenAlexaboutno aff
D. Lee Hamilton, Roberta Heale, Laura Hill, Robyn Gorham

Bibliographic record

VenueCanadian Nurse Practitioner Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaTerm (time)Long-term careMedicinePsychologyGerontologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Aim: The aim of this article is to explore the behaviours related to pain for residents of long-term care with dementia. Background: Nurse practitioners caring for residents with dementia face the complex task of assessing and managing pain. Residents in long-term care who live with dementia may express pain differently than those without cognitive or communication impairments. Responsive behaviours that may occur in advancing dementia may also be indicators of pain. Tools that consider behaviours related to pain for cognitively impaired residents such as the PACSLAC, should be considered. Methods: This secondary analysis is a retrospective population-based descriptive study of Resident Assessment Instrument-Minimum Data Set version 2.0 assessments conducted in long-term care homes across Ontario. Findings: Results show that, in many circumstances when a resident with dementia exhibits responsive behaviors that may be related to both dementia and pain, no pain is reported. These items include wandering, resisting care, and repetitive verbalizations. The findings suggest that pain may not be identified or treated in people with dementia. Conclusions: Nurse practitioners must provide an individualized approach in order to accurately assess and treat pain for residents with dementia. By acknowledging deficits and improving practice guidelines, the hope is to improve pain management and quality of life for residents with dementia and pain.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.322
Teacher spread0.289 · 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.

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
Published2022
Admission routes1
Has abstractyes

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