Exploring Social Media Group Use Among Breastfeeding Mothers: Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Breastfeeding is well known as the optimal source of nutrition for the first year of life. However, suboptimal exclusively breastfeeding rates in the United States are still prevalent. Given the extent of social media use and the accessibility of this type of peer-to-peer support, the role of social networking sites in enabling and supporting breastfeeding mothers needs to be further explored. OBJECTIVE: This study aimed to leverage mothers' attitudes and behaviors of social media usage to understand effects on breastfeeding outcomes. METHODS: Participants were recruited from 1 probreastfeeding social media group with over 6300 members throughout the United States. Online focus group discussions were conducted with 21 women; interviews were conducted with 12 mothers. Qualitative data were aggregated for thematic analysis. RESULTS: Participants indicated that the social media group formed a community of support for breastfeeding, with normalizing breastfeeding, empowerment for breastfeeding, resource for breastfeeding, and shared experiences in breastfeeding as additional themes. CONCLUSIONS: According to participants, social media groups can positively influence breastfeeding-related attitudes, knowledge, and behaviors as well as lead to longer duration of breastfeeding. The results of this study should be taken into account when designing interventions for breastfeeding mothers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".