Possibilities and Challenges of Delivering Health-Related Small Group Interventions Online: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The outbreak of the COVID-19 pandemic required the transition of health-related face-to-face group interventions to an online setting. While it seems that group outcomes can be realized in an online setting, less is known about resulting potential challenges (and advantages) and how these can be overcome. OBJECTIVE: The aim of this article is to explore what challenges and advantages may arise when providing health-related small group interventions in an online setting and how to overcome these challenges. METHODS: Scopus and Google Scholar databases were searched for relevant literature. Effect studies, meta-analyses, literature reviews, theoretical frameworks, and research reports relating to synchronous, face-to-face, health-related small group interventions, online group interventions, and video teleconferencing group interventions were identified and screened. Findings relating to potential challenges and corresponding strategies are described. In addition, potential advantages of online group settings were explored. Relevant insights were gathered until saturation of results relating to the research questions was reached. RESULTS: The literature indicated several aspects that require extra attention and preparation in the online group setting. These include the delivery of nonverbal communication and affect regulation, as well as the build-up of group cohesion and therapeutic alliance, which seem more challenging online. Yet there are strategies to overcome these challenges, such as metacommunication, collecting participant feedback, and providing guidance concerning technical accessibility. In addition, the online setting provides opportunities to reinforce group identity, such as by allowing independence and the ability to create homogeneous groups. CONCLUSIONS: While online, health-related small group interventions offer a considerable number of possibilities and benefits compared to face-to-face groups, there are also potential drawbacks to consider, which, if anticipated, can be to a great extent overcome.
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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 | 0.063 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".