Parents’ online discussions about children’s dental caries: A critical content analysis
Bibliographic record
Abstract
OBJECTIVES: Through an analysis of postings to an online parenting forum, we aimed to explore the many ways in which parents orient to (i.e., take up, challenge, re-articulate) information about child dental health in the context of their online interactions. Our analysis is anchored in Nettleton's theoretical work on dental authority and power, which we apply in a digital context. METHODS: We examined discussion threads from the public online forums on BabyCenter Canada. We identified relevant threads using the site search function and keywords related to dental health, with a focus on dental caries (tooth decay), related care behaviours (e.g., toothbrushing), and the controversial issue of fluoride. Following descriptive content coding, we applied a critical lens to unpack themes related to expert knowledge, gender and parenting online cultures. RESULTS: We analyzed 479 relevant threads. Our findings focus on two central themes: the tension between parents' views and those of dental health professionals; and, the gendered, cultural roles and expectations that position mothers as primarily responsible for the care of children's dental health. Though these themes are not new, our findings show that they persist in the digital context where social divisions (e.g., expert/non-expert) may be blurred. CONCLUSIONS: Our analysis of online discussions provides an opportunity to think critically about ways in which parents engage with public health, in digital contexts. Although some mothers express disconnect when communicating with dental professionals, they are very engaged and concerned with dental health issues for their children. A challenge for dental public health is to find ways to shift perspective towards recognizing that the target population is empowered and already engaged in discussions of research evidence and clinical encounters on their own terms, facilitated by an online context.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".