Adapting a Mental Health Intervention for Adolescents During the COVID-19 Pandemic: Web-Based Synchronous Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Although focus groups are a valuable qualitative research tool, face-to-face meetings may be difficult to arrange and time consuming. This challenge has been further compounded by the global COVID-19 pandemic and the subsequent lockdown and physical distancing measures implemented, which caused exceptional challenges to human activities. Online focus groups (OFGs) are an example of an alternative strategy and require further study. At present, OFGs have mostly been studied and used in high-income countries, with little information relating to their implementation in low- and middle-income countries (LMICs). OBJECTIVE: The aim of this study is to share our experiences of conducting OFGs through a web conferencing service and provide recommendations for future research. METHODS: As part of a broader study, OFGs were developed with adults and adolescents in Colombia during the COVID-19 pandemic. Through a convenience sampling method, we invited eligible participants via email in two different cities of Colombia to participate in OFGs conducted via Microsoft Teams. Researcher notes and discussion were used to capture participant and facilitator experiences, as well as practical considerations. RESULTS: Technical issues were encountered, but various measures were taken to minimize them, such as using a web conferencing service that was familiar to participants, sending written instructions, and performing a trial meeting prior to the OFG. Adolescent participants, unlike their adult counterparts, were fluent in using web conferencing platforms and did not encounter technical challenges. CONCLUSIONS: OFGs have great potential in research settings, especially during the current and any future public health emergencies. It is important to keep in mind that even with the advantages that they offer, technical issues (ie, internet speed and access to technology) are major obstacles in LMICs. Further research is required and should carefully consider the appropriateness of OFGs in different settings.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".