Help-Seeking, Support, and Engagement in Gestational Diabetes Mellitus Online Communities on Facebook: Content Analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence of gestational diabetes mellitus (GDM) has drastically risen in recent years. For some, self-management includes the use of GDM online communities on Facebook. Such communities can fill gaps in information and support that participants are not able to access elsewhere to address unmet needs. Given the popularity of sharing information about pregnancy on Facebook and the documented benefits of diabetes online communities, the same may be true of GDM online communities. OBJECTIVE: This study aimed to categorize and quantify what is being discussed in GDM Facebook groups, including informational and emotional help-seeking behavior, and how this support and engagement may be demonstrated by peers through comments and reactions. METHODS: We sourced the data from the 2 largest Facebook groups focused on GDM in Australia. A summative content analysis was conducted on original posts across the 2 groups and coded for topics as well as help-seeking types. The coding scheme was based on the previous work of Liang and Scammon. Visible indicators of engagement, including the number of comments and "reactions," were tabled and manually evaluated. RESULTS: There were 388 original posts, and the analysis produced 6 topics: GDM self-management (199/388, 51.3%), GDM clinical management (120/388, 30.9%), preparing for birth (40/388, 10.3%), mental distress (35/388, 9%), birth announcement (29/388, 7.5%), and GDM journey reflections (21/388, 5.4%). Secondary coding of help-seeking type revealed more than half of the posts were informational help-seeking (224/388, 57.7%), while a small proportion were both informational and emotional help-seeking (44/388, 11.3%), and some (12/388, 3.1%) were emotional help-seeking only. Self-disclosure was identified as a fourth category, comprising almost a quarter of all posts (90/388, 23.2%). A total of 6022 comments were posted in response to the original posts, and there were 4452 reactions across all posts. Emotional help-seeking attracted the most comments per thread (mean 21.5, SD 19.8), followed by informational and emotional help-seeking (mean 20.2, SD 14.7), informational help-seeking (mean 15.6, SD 14.6), and self-disclosure (mean 14.3, SD 21.8). Across all help-seeking categories, few reactions occurred compared to comments; in contrast, self-disclosure attracted a large number of reactions (mean 9.4, SD 45.3). CONCLUSIONS: This is one of the first studies to examine peer support in a GDM online community on Facebook. Our findings suggest that active participants' needs around information and support in relation to GDM are being somewhat met by peer-led online communities. Given the practical limitations of formal health care, including the provision of ongoing social support, it is important to recognize how GDM online communities can complement formal health care and help address unmet needs.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".