Use of Web Conferencing Technology for Conducting Online Focus Groups Among Young People With Lived Experience of Suicidal Thoughts: Mixed Methods Research
Bibliographic record
Abstract
BACKGROUND: There is an increasing interest in engaging people with lived experience in suicide prevention research. However, young people with suicidal thoughts have been described as a "hard-to-include" population due to time, distance, stigma, and social barriers. OBJECTIVE: This study aims to investigate whether conducting synchronous Web conferencing technology-based online focus groups (W-OFGs) is a feasible method to engage young people with lived experience of suicidal thoughts in suicide prevention research. METHODS: Young people aged between 16 and 25 years and living in Sydney, Australia, were recruited through flyers, emails, and social media advertisements. The W-OFGs were established using a Web conferencing technology called GoToMeeting. Participants' response rate, attendance, and feedback of the W-OFGs were analyzed to determine whether the W-OFG system is feasible for suicide prevention research. Researchers' reflections about how to effectively implement the W-OFGs were also reported as part of the results. RESULTS: In the pre-W-OFG survey, 39 (97.5%) young people (n=40) chose to attend the online focus group. Among the 22 participants who responded to the W-OFG invitations, 15 confirmed that they would attend the W-OFGs, of which 11 participants attended the W-OFGs. Feedback collected from the participants in the W-OFG and the post-W-OFG survey suggested that online focus groups are acceptable to young people in suicide prevention research. Considerations for selecting the Web conferencing platform, conducting the mock W-OFGs, implementing the risk management procedure, inviting participants to the W-OFGs, and hosting and moderating the W-OFGs as well as a few potential ethical and pragmatic challenges in using this method are discussed in this study. CONCLUSIONS: The Web conferencing technology provides a feasible replacement for conventional methods, particularly for qualitative research involving vulnerable populations and stigmatized topics including suicide prevention. Our results indicate that this modality is an optimal alternative to engage young people in the focus group discussion. Future studies should compare the data collected from the Web conferencing technology and conventional face-to-face methods in suicide prevention research to determine if these two methods are equivalent in data quality from a quantitative approach.
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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 | 0.070 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".