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Record W4318332748 · doi:10.1080/09638288.2023.2169775

A qualitative interview study exploring the psychological health impacts of the SPIN-CHAT program among people with systemic sclerosis at the onset of COVID-19: perceptions of trial participants and research team members

2023· article· en· W4318332748 on OpenAlexafffund
Amanda Wurz, Delaney Duchek, Kelsey Ellis, Mannat Bansal, Marie‐Eve Carrier, Lydia Tao, Laura Dyas, Linda Kwakkenbos, Brooke Levis, Ghassan El‐Baalbaki, Danielle B. Rice, Yin Wu, Richard S. Henry, Laura Bustamante, Sami Harb, Shannon Hebblethwaite, Scott B. Patten, Susan J. Bartlett, John Varga, Luc Mouthon, Sarah Markham, Brett D. Thombs, S. Nicole Culos‐Reed

Bibliographic record

VenueDisability and Rehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsMcGill University Health CentreConcordia UniversityAlberta Health ServicesUniversité du Québec à MontréalUniversity of CalgaryMcGill UniversityOntario Brain InstituteJewish General HospitalUniversity of the Fraser Valley
FundersCanadian Institutes of Health ResearchAlberta InnovatesMitacs
KeywordsThematic analysisPsychologyOnline chatFeelingMedical educationQualitative researchMedicineSocial psychologyThe Internet

Abstract

fetched live from OpenAlex

PURPOSE: Explore trial participants' and research team members' perceptions of the impact of the videoconference-based, supportive care program (SPIN-CHAT Program) during early COVID-19 for individuals with systemic sclerosis (SSc). METHODS: Data were collected cross-sectionally. A social constructivist paradigm was adopted, and one-on-one videoconference-based, semi-structured interviews were conducted with SPIN-CHAT Trial participants and research team members. A hybrid inductive-deductive approach and reflexive thematic analysis were used. RESULTS: Of the 40 SPIN-CHAT Trial participants and 28 research team members approached, 30 trial participants (Mean age = 54.9; SD = 13.0 years) and 22 research team members agreed to participate. Those who took part in interviews had similar characteristics to those who declined. Five themes were identified: (1) The SPIN-CHAT Program conferred a range of positive psychological health outcomes, (2) People who don't have SSc don't get it: The importance of SSc-specific programming, (3) The group-based format of the SPIN-CHAT Program created a safe space to connect and meet similar others, (4) The structure and schedule of the SPIN-CHAT Program reduced feelings of boredom and contributed to enhanced psychological health, (5) The necessity of knowledge, skills, and tools to self-manage SSc and navigate COVID-19. CONCLUSION: Participants' and research team members' perspectives elucidated SPIN-CHAT Program benefits and how these benefits may have been realized. Results underscore the importance of social support from similar others, structure, and self-management to enhance psychological health during COVID-19. TRIAL REGISTRATION: clinicaltrials.gov (NCT04335279)IMPLICATIONS FOR REHABILITATIONThe videoconference-based, supportive care SPIN-CHAT Program enhanced psychological health amongst individuals affected by systemic sclerosis.SPIN-CHAT Program participants and research team members shared that being around similar others, program structure, and self-management support were important and may have contributed to enhanced psychological health.Further efforts are required to explore experiences within supportive care programs to better understand if and how psychological health is impacted.

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)0.028
metaresearch head score (Gemma)0.034
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 score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.005
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0020.003
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 head0.299
GPT teacher head0.505
Teacher spread0.206 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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