Dental treatment improves the oral health‐related quality of life of adolescents: A mixed‐methods approach
Bibliographic record
Abstract
Abstract Aim To evaluate and understand the impact of dental treatment on oral health‐related quality of life (OHRQoL) of adolescents. Design A sequential explanatory mixed‐methods design was performed. A sample of 182 adolescents, aged 10 and 15 years old who had finished their dental treatment at adolescent dental clinic of Federal University of Santa Maria from 2010 to 2016, were included. Participants answered the short form of Child Perceptions Questionnaire (CPQ11‐14) prior to their dental treatment and 1 month after concluding the treatment. The effect size was calculated to assess magnitude of change. In qualitative phase, semi‐structured interviews took place at the end of the dental treatment. Interviews were audio‐recorded and analyzed according to thematic analysis following Braun and Clarke. Results The effect sizes ranged from 0.35 to 1.00, and the oral symptom domain presented the greatest effect. Sixteen interviews were conducted and five themes emerged: concept of quality of life, oral health influenced by oral conditions, oral health symptoms influencing seeking care behavior, personal and subjective experiences, and dental educational environment. Conclusion Dental treatment has an uncountable meaning for adolescents because it has a psychosocial meaning in this phase of life and it is able to improve their OHRQoL.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".