Black Girls Run Too: A Content Analysis of the Black Girls Run National Facebook Group
Bibliographic record
Abstract
Background Recent evidence suggests that 59%-73% of Black women are not reaching recommended targets for physical activity (PA). PA is a key modifiable lifestyle factor that can help mitigate risk for chronic diseases such as obesity, diabetes, and hypertension, which disproportionately affect Black women. Web-based communities focused on PA have been emerging in recent years as digital gathering spaces to provide support for PA in specific populations. Objective The purpose of this study was to conduct a content analysis of the Black Girls Run (BGR) Facebook page, which is devoted to promoting PA in Black women and has over 230,000 followers. Such data can inform future social media–based interventions. Methods We collected 397 posts and associated engagement data from the national BGR Facebook page for the 6-month period between June 1 and December 31, 2021. We then conducted a content analysis of these posts and examined which types of posts elicited the most engagement. Results The content analysis revealed 8 categories of posts: shout-outs (30.7%), goals or motivational posts (16.3%), announcements (15.9%), sponsored posts or advertisements (13.6%), health-related posts (11.0%), the lived Black experience posts (5.79%), self-care posts (3.78%), and holiday-related posts or greetings (2.02%). These 397 posts attracted a total of 55,573 engagements. Of these, 33,560 were “reactions” (eg, likes) and 5082 were shares. Shout-outs elicited the highest engagement (22,268 engagements), followed by goals or motivational posts (11,490 engagements). Conclusions The majority of content on the BGR Facebook page (62.9%) was focused on celebrating member achievements, motivating members to become active, and announcing and promoting active events. This content also attracted 75% of the engagement on this page. BGR appears to be a rich web-based community that offers social support for PA as well as culturally relevant health and social justice content. Web-based communities may be uniquely positioned to engage minoritized populations in health behavior. Further research should explore how to best leverage web-based communities in interventions to increase PA and other lifestyle behaviors. Conflicts of Interest None declared.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".