Using Social Media for Qualitative Health Research in Danish Women of Reproductive Age: Online Focus Group Study on Facebook
Bibliographic record
Abstract
BACKGROUND: Social media platforms provide new possibilities within health research. With Facebook being the largest social network in the world, it constitutes a potential platform for recruitment and data collection from women of reproductive age. Women in Denmark and in other Western countries postpone motherhood and risk infertility due to their advanced age when they try to conceive. To date, no study has explored Danish women's reflections on the timing of motherhood within a social media setting. OBJECTIVE: The aim of this study was to explore the challenges and opportunities of using Facebook as a platform for qualitative health research in Danish women of reproductive age. METHODS: This study was a qualitative study based on 3 online focus groups on Facebook with 26 Danish women of reproductive age discussing the timing of motherhood in January 2020. RESULTS: Conducting online focus groups on Facebook was successful in this study as the web-based approach was found suitable for developing qualitative data with women of reproductive age and made recruitment easy and free of charge. All participants found participating in an online focus group to be a positive experience. More than half of the women participating in the online focus groups found it advantageous to meet on Facebook instead of meeting face-to-face. CONCLUSIONS: Conducting online focus groups on Facebook is a suitable method to access qualitative data from women of reproductive age. Participants were positive toward being a part of an online focus group. Online focus groups on social media have the potential to give women of reproductive age a voice in the debate of motherhood.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".