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Record W2286054644 · doi:10.6000/1927-5129.2016.12.10

Exercise and Mindfulness-Based-Stress-Reduction: A Multidimensional Approach Towards Cancer Survivorship Care

2016· article· en· W2286054644 on OpenAlexvenueno aff
Timothy Marshall

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2016
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialPsychological interventionMindfulnessAnxietySurvivorship curveQuality of life (healthcare)DistressCancerMedicineMindfulness-based stress reductionClinical psychologyPsychologyPhysical therapyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Cancer survivors often experience a variety of physiological deficits resulting from cancer treatment such as reduced muscle strength, decreased range of motion and poor balance. Cancer survivors also commonly experience psychosocial side effects, such as anxiety, depression and fear of recurrence. Overall, it is common for cancer survivors to report a decrease in physical and emotional wellbeing and overall quality of life. Research suggests that improvements in physical health can be achieved through moderate intensity exercise such as light resistance training and moderate aerobic exercise in this population. Mindfulness-Based-Stress-Reduction (MBSR) programming utilizes various mind/body techniques that can reduce state anxiety levels, distress and depression. While cancer survivors face numerous physiological and psychological challenges, exercise interventions focus on physical health, while MBSR interventions focus on psychosocial health. The American Medical Association (AMA) recommends a patient’s care should include psychological, physiological, psychosocial and educational components, emphasizing the need for an integrated approach to cancer survivorship. Integrating exercise and MBSR interventions may serve to optimize the overall health and quality of life of a cancer survivor.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.326
Teacher spread0.285 · 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 designOther design
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
Published2016
Admission routes1
Has abstractyes

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