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Record W3144325870 · doi:10.2196/27384

Virtual Mind-Body Programming for Patients With Cancer During the COVID-19 Pandemic: Qualitative Study

2021· article· en· W3144325870 on OpenAlexvenueno aff
Nicholas Emard, Kathleen Lynch, Kevin T. Liou, Thomas M. Atkinson, Angela K. Green, Bobby Daly, Kelly M. Trevino, Jun J. Mao

Bibliographic record

VenueJMIR Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institute on AgingNational Institutes of HealthMemorial Sloan-Kettering Cancer Center
KeywordsPsychosocialCoping (psychology)AnxietyGrounded theoryPsychologyQualitative researchSocial isolationStressorSocial supportClinical psychologyDistancingPsychotherapistMedicineCoronavirus disease 2019 (COVID-19)Psychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with cancer are particularly vulnerable to stress and anxiety during the COVID-19 pandemic. Social distancing is critical for patients with cancer; however, it can also reduce their access to psychosocial coping resources. OBJECTIVE: The aim of this study was to explore patient experiences to generate a model of how virtual mind-body programs can support the psychosocial well-being of patients with cancer. METHODS: We conducted a qualitative study among patients (aged ≥18 years) who participated in a virtual mind-body program offered by a National Cancer Institute-designated Comprehensive Cancer Center during the COVID-19 pandemic. The program consisted of mind-body group therapy sessions of fitness, yoga, tai chi, dance therapy, music therapy, and meditation. Live integrative medicine clinicians held each session via Zoom videoconferencing for 30-45 minutes. In semistructured phone interviews (n=30), patients were asked about their overall impressions and perceptions of the benefits of the sessions, including impacts on stress and anxiety. Interviews were analyzed using grounded theory. RESULTS: Among the 30 participants (average age 64.5 years, SD 9.36, range 40-80, 29 female), three major themes were identified relating to experiences in the virtual mind-body program: (1) the sessions helped the patients maintain structured routines and motivated them to adhere to healthy behaviors; (2) the sessions enhanced coping with COVID-19-related-stressors, allowing patients to "refocus" and "re-energize"; and (3) the sessions allowed patients to connect, fostering social relationships during a time of isolation. These themes informed the constructs of a novel behavioral-psychological-social coping model for patients with cancer. CONCLUSIONS: Virtual mind-body programming supported patients with cancer during the COVID-19 pandemic through a behavioral-psychological-social coping model by enhancing psychological coping for external stressors, supporting adherence to motivation and health behaviors, and increasing social connection and camaraderie. These programs have potential to address the behavioral, psychological, and social challenges faced by patients with cancer during and beyond the COVID-19 pandemic. The constructs of the conceptual model proposed in this study can inform future interventions to support isolated patients with cancer. Further clinical trials are needed to confirm the specific benefits of virtual mind-body programming for the psychosocial well-being and healthy behaviors of patients with cancer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.413
Teacher spread0.364 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations32
Published2021
Admission routes1
Has abstractyes

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