MétaCan
Menu
Back to cohort
Record W4402994678 · doi:10.4103/jrms.jrms_17_23

Evaluating the effects of humor therapy on fatigue levels of hemodialysis patients: A single-blind, randomized clinical trial study

2024· article· en· W4402994678 on OpenAlexaff
Mohammad Sahebkar, Mojgan Ansari, Farnush Attarzadeh, Fateme Borzoee

Bibliographic record

VenueJournal of Research in Medical Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Ottawa
FundersSabzevar University of Medical Sciences
KeywordsMedicineRandomized controlled trialAnalysis of varianceHemodialysisClinical trialInternal medicineSingle blindPhysical therapy

Abstract

fetched live from OpenAlex

Background: This study investigated the effects of humor therapy on the fatigue levels of patients receiving hemodialysis (HD). Materials and Methods: A single-blind, randomized clinical trial of 66 HD patients for 3 weeks was conducted, in which two groups were randomly allocated – humor therapy and control. In the intervention group, humor therapy sessions were conducted twice a week for 3 weeks. As a pre- and postintervention assessment, the Fatigue Symptom Inventory (FSI) was completed. Results: According to the repeated-measures ANOVA test, FSI values exhibited a significant decline in the humor therapy group and an increase in the control group at the first, second, and third visits (humor therapy vs. control: 30.38 ± 8.75 and 61.80 ± 13.92, P < 0.001; 35.71 ± 10.05 and 69.53 ± 15.32, P < 0.001; and 34.85 ± 9.24 and 70.34 ± 22.26, P < 0.001, respectively) compared with baseline (humor therapy vs. control: 49.26 ± 5.19 and 52.09 ± 11.69, P = 0.204). Conclusion: Findings suggest that humor therapy can effectively reduce fatigue levels in patients presenting with HD.

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)0.105
metaresearch head score (Gemma)0.051
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1050.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.492
GPT teacher head0.601
Teacher spread0.109 · 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; both teacher heads agree on what is shown here.

Study designRandomized trial
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research in Medical SciencesSame topicDialysis and Renal Disease ManagementFrench-language works237,207