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Record W2614033428

The Effects of a Therapeutic Recreation Program on Overall Well-being Among Older-adults with Alzheimer Disease and Their Care Partner

2017· dissertation· en· W2614033428 on OpenAlexfundaboutno aff
Laura Rolph

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2017
Typedissertation
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsRecreationDiseaseGerontologyRecreational therapyMedicineWell-beingPsychologyAlzheimer's diseasePsychotherapistInternal medicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Background: With the increased pressures that our aging population has on our country, this study looks at the effects that an 8 week Therapeutic Recreation infused program has on the well-being for both individuals with Alzheimer Disease and their Care Partner. 
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\nMethod: Sample of the study is from a single secondary data set. Sampling criteria for the study was individuals with early to mid-stage signs of Alzheimer Disease or other dementias and their Care Partner from across 6 different locations in Ontario, Canada. Pre and Post data from the Warwick-Edinburgh Mental Well-Being Scale was analyzed through a repeated measures ANOVA.
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\nResults: Care Partners indicated an increase in well-being slightly higher than that of the Persons With Dementia. This slight increase in well-being was not statistically significant for either the Care Partner or the Person’s with Dementia. Unexpected ANOVA findings revealed that there was a significant between-subject effect as Care Partners showed a higher overall level of well-being. This further emphasizes the importance for early intervention for Persons with Dementia. 
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\nConclusion: Overall this program is in the early stages of development. It is still believed that program modifications could facilitate a cost-effective intervention for communities.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score1.000

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 studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.290
Teacher spread0.278 · 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
Published2017
Admission routes2
Has abstractyes

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