Public Perspectives of Oral and Maxillofacial Injuries Related to Domestic Abuse Experiences and Help‐Seeking Barriers: Web Scraping of Reddit Posts
Bibliographic record
Abstract
BACKGROUND: Domestic abuse (DA) frequently results in injuries to the head, neck, and orofacial regions. Despite the visibility of these injuries, many survivors do not access formal medical or dental care because of fear, stigma, or systemic barriers. Reddit, an anonymous online platform, offers a unique opportunity to examine unfiltered victims/survivors' narratives shared in public forums. The aim of this study was to explore how DA, particularly its physical, psychological, and social impacts, was represented, perceived, and discussed on Reddit. Special attention was given to posts describing injuries to the head, neck, and orofacial region, to understand how victims/survivors narrated their experiences, sought support, and navigated disclosure in anonymous digital spaces. METHODS: This study employed web scraping to analyze Reddit posts from four domestic abuse-related subreddits (r/AbuseInterrupted, r/DomesticAbuse, r/DomesticViolence, and r/domesticviolence) using Python's Reddit API Wrapper (PRAW). Posts were filtered using anatomical keywords relevant to dental and maxillofacial trauma. After cleaning and manual review, first-person accounts referencing injuries to the head, neck, or orofacial area underwent qualitative thematic analysis and quantitative content analysis. RESULTS: A total of 588 Reddit posts related to DA were initially collected. Of the 588 posts, 153 (26.0%) met the inclusion criteria and were retained for analysis. Analysis of the 153 posts meeting the inclusion criteria revealed the most affected regions in DA victims, with frequent descriptions of physical abuse including slapping, grabbing, strangulation, and blunt-force trauma. Thematic analysis identified four central themes: (1) visible injuries, (2) barriers to accessing medical and dental care, (3) psychological and emotional consequences of abuse, and (4) inconsistent responses from healthcare and legal systems. CONCLUSIONS: Oral and Maxillofacial injuries may serve as critical red flags of domestic abuse. Even when visible, they are often overlooked by healthcare providers. The findings of this study underscore the need for trauma-informed training among dental professionals and support the integration of domestic abuse screening protocols into routine oral health care. Additionally, the ethical use of web scraping presents a valuable tool for public health research by amplifying survivor voices and helping to identify intervention gaps that may be missed in clinical or institutional data.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".