Transitioning to Remote Clinic Visits in a Smoking Cessation Trial During the COVID-19 Pandemic: Mixed Methods Evaluation
Bibliographic record
Abstract
BACKGROUND: The pandemic of SARS-CoV-2, which causes COVID-19, has caused disruptions in ongoing clinical trials and is expected to accelerate interest in conducting research studies remotely. OBJECTIVE: A quasi-experimental, mixed methods approach was used to examine the rates of visit completion as well as the opinions and experiences of participants enrolled in an ongoing clinical trial of smoking cessation who were required to change from in-person clinic visits to remote visits using video or telephone conferencing due to the COVID-19 pandemic. METHODS: For quantitative comparisons, we used a quasi-experimental design, comparing a cohort of participants followed during the pandemic (n=23, COVID-19 cohort) to a comparable cohort of participants followed over a similar time period in the calendar years 2018 and 2019 (n=51, pre-COVID-19 cohort) to examine the rates of completion of scheduled visits and biospecimen collection. For the qualitative component, interviews were conducted with participants who experienced the transition from in-person to remote visits. RESULTS: Participants in the COVID-19 cohort completed an average of 83.6% of remote clinic visits (95% CI 73.1%-91.2%), which was not significantly different than the in-person completion rate of 89.8% in the pre-COVID-19 cohort. Participants in the COVID-19 cohort returned an average of 93.2% (95% CI 83.5%-98.1%) of saliva specimens for remote clinic visits completed, which was not significantly different than the in-person saliva specimen completion rate of 100% in the pre-COVID-19 cohort. Two broad themes emerged from the qualitative data: (1) the benefits of remote visits and (2) the challenges of remote counseling compared to in-person counseling. Despite limited experience with telehealth prior to this transition, most participants expressed a willingness to engage in remote visits in the future. CONCLUSIONS: Even in the context of a rapid transition from in-person to remote visits necessitated by the COVID-19 pandemic, rates of visit completion and return of biospecimens remained high. Participants were generally accepting of the transition. Further research is needed to identify the optimal mix of in-person and remote visits beyond the pandemic context and to better understand how these changes may impact study outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03262662; https://clinicaltrials.gov/ct2/show/study/NCT03262662.
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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 | 0.140 | 0.142 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".