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Record W4402695989 · doi:10.1177/03080226241283288

The effect of self-management program performed via telerehabilitation on occupational performance and satisfaction of individuals with systemic sclerosis

2024· article· en· W4402695989 on OpenAlexaboutno aff
Emirhan Karakuş, Fulden Sarı, Aslıhan Avanoğlu Güler, Deran Oskay, Çiğdem Öksüz

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsnot available
FundersHacettepe Üniversitesi
KeywordsOccupational therapyTelerehabilitationMedicinePhysical therapyIntervention (counseling)Life satisfactionActivities of daily livingSelf-managementClinical psychologyPsychologyNursingHealth careTelemedicine

Abstract

fetched live from OpenAlex

Introduction: Individuals with systemic sclerosis experience difficulties in perceived occupational performance and satisfaction in daily life activities, largely due to the various symptoms associated with the disease. Methods: Twenty-nine individuals with systemic sclerosis participated in the study. Occupational performance and satisfaction levels were evaluated before and after the program were performed using the Canadian Occupational Performance Measure. The self-management program, guided by the COPM activities, was tailored to the patients' needs and informed by relevant literature. The program consisted of eight sessions over 8 weeks, with one 45-minute session per week. Results: < 0.001). Analysis of the occupation distributions revealed that self-care constituted 31.97%, productivity 45.90%, and leisure time 22.13%. Conclusion: The self-management program delivered via telerehabilitation positively impacted the perceived occupational performance and satisfaction of individuals with systemic sclerosis. It is believed that self-management intervention can be effectively used to enhance the occupational performance and satisfaction of these individuals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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