MétaCan
Menu
Back to cohort
Record W2136366257 · doi:10.1177/0193945907305126

Therapeutic Touch and Agitation in Individuals With Alzheimer's Disease

2007· article· en· W2136366257 on OpenAlexaff
Pamela Hawranik, Judith Deatrich

Bibliographic record

VenueWestern Journal of Nursing Research · 2007
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsHealth Sciences CentreUniversity of Manitoba
Fundersnot available
KeywordsAggressionTherapeutic touchDementiaIntervention (counseling)PsychologyDiseaseClinical psychologyTherapeutic effectMedicineDevelopmental psychologyPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Limited effective strategies exist to alleviate or treat disruptive behaviors in people with Alzheimer's disease. Fifty-one residents of a long-term care facility with Alzheimer's disease were randomly assigned to one of three intervention groups. A multiple time series, blinded, experimental design was used to compare the effectiveness of therapeutic touch, simulated therapeutic touch, and usual care on disruptive behavior. Three forms of disruptive behavior comprised the dependent variables: physical aggression, physical nonaggression, and verbal agitation. Physical nonaggressive behaviors decreased significantly in those residents who received therapeutic touch compared with those who received the simulated version and the usual care. No significant differences in physically aggressive and verbally agitated behaviors were observed across the three study groups. The study provided preliminary evidence for the potential for therapeutic touch in dealing with agitated behaviors by people with dementia. Researchers and practitioners must consider a broad array of strategies to deal with these behaviors.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.215
GPT teacher head0.540
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), 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

Citations81
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueWestern Journal of Nursing ResearchSame topicHealthcare Decision-Making and RestraintsFrench-language works237,207