Exploring research team members’ and trial participants’ perceptions of acceptability and implementation within one videoconference-based supportive care program for individuals affected by systemic sclerosis during COVID-19: a qualitative interview study
Bibliographic record
Abstract
The SPIN-CHAT Program was designed to support mental health among individuals with systemic sclerosis (SSc; commonly known as scleroderma) and at least mild anxiety symptoms at the onset of COVID-19. The program was formally evaluated in the SPIN-CHAT Trial. Little is known about program and trial acceptability, and factors impacting implementation from the perspectives of research team members and trial participants. Thus, the propose of this follow-up study was to explore research team members' and trial participants' experiences with the program and trial to identify factors impacting acceptability and successful implementation. Data were collected cross-sectionally through one-on-one, videoconference-based, semi-structured interviews with 22 research team members and 30 purposefully recruited trial participants (Mage = 54.9, SD = 13.0 years). A social constructivist paradigm was adopted, and data were analyzed thematically. Data were organized into seven themes: (i) getting started: the importance of prolonged engagement and exceeding expectations; (ii) designing the program and trial: including multiple features; (iii) training: research team members are critical to positive program and trial experiences; (iv) offering the program and trial: it needs to be flexible and patient-oriented; (v) maximizing engagement: navigating and managing group dynamics; (vi) delivering a videoconference-based supportive care intervention: necessary, appreciated, and associated with some barriers; and (vii) refining the program and trial: considering modification when offered beyond the period of COVID-19 restrictions. Trial participants were satisfied with and found the SPIN-CHAT Program and Trial to be acceptable. Results offer implementation data that can guide the design, development, and refinement of other supportive care programs seeking to promote psychological health during and beyond COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".