Life course experiences and lay diagnosis explain low‐income parents' child dental decisions: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to better understand low-income parents' child dental care decisions through a life course approach that captured parents' experiences within the social context of poverty. METHODS: We conducted 43 qualitative life history interviews with 10 parents, who were long-term social assistance recipients living in Montreal, Canada. Thematic analysis involved interview debriefing, transcript coding, theme identification and data interpretation. RESULTS: Our interviews identified two emergent themes: lay diagnosis and parental oral health management. Parents described a process of 'lay diagnosis' that consisted of examining their children's teeth and interpreting their children's oral signs and symptoms based on their observations. These lay diagnoses were also shaped by their own dental crises, care experiences and oral health knowledge gained across a life course of poverty and dental disadvantage. Parents' management strategies included monitoring and managing their children's oral health themselves or by seeking professional recourse. Parents' management strategies were influenced both by their lay diagnoses and their perceived ability to manage their children's oral health. Parents felt responsible for their children's dental care, empowered to manage their oral health and sometimes forgo dental visits for their children because of their own self-management life history. CONCLUSION: This original approach revealed insights that help to understand why low-income parents may underutilize free dental services. Further research should consider how dental programs can nurture parental empowerment and capitalize on parents' perceived ability to diagnose and manage their children's oral health.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".