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Record W2017257925 · doi:10.1188/14.onf.257-264

The Impact of Yoga on Quality of Life and Psychological Distress in Caregivers for Patients With Cancer

2014· article· en· W2017257925 on OpenAlexaff
Andi Céline Martin, Melanie R. Keats

Bibliographic record

VenueOncology nursing forum · 2014
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsDalhousie UniversitySaskatchewan HealthUniversity of Regina
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Psychological interventionPsychological distressDistressIntervention (counseling)Mental healthPhysical therapyFamily caregiversClinical psychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

PURPOSE/OBJECTIVES: To assess the effects of a six-week Vinyasa yoga (VY) intervention on caregivers' overall quality of life (QOL) and psychological distress. DESIGN: A single-group, pre- and post-test pilot study. SETTING: University public recreational facility. SAMPLE: 12 informal caregivers for patients with cancer. METHODS: Caregivers participated in a six-week VY intervention and completed measures of QOL and psychological distress pre- and postintervention. Program satisfaction was measured with open-ended survey questions. MAIN RESEARCH VARIABLES: QOL, psychological distress, and program satisfaction. FINDINGS: Significant improvements were found in the mental component score of overall QOL and in overall psychological distress. Several subdomains of QOL and psychological distress were also improved significantly. Open-ended survey question responses revealed participants perceived physical and mental benefit from the intervention, highlighting improvements in flexibility, core and upper-body strength, balance, breathing, and energy. CONCLUSIONS: Informal caregivers may benefit mentally and physically from participating in VY. IMPLICATIONS FOR NURSING: Caregivers of patients with cancer characterize a group worthy of attention, research, and interventions focusing on their healthcare needs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.250

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 studies0.0000.000
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 head0.052
GPT teacher head0.441
Teacher spread0.389 · 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

Citations36
Published2014
Admission routes1
Has abstractyes

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