Impact of Sense of Coherence on Oral Health Behaviors: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: The aim of this review was to critically analyze the empirical evidence on the association between Sense of Coherence (SOC) and oral health behaviors through a systematic approach. METHODS: A systematic search up to April 2015 was carried out using the following electronic bibliographic databases: PubMed, Ovid MEDLINE; ISI Web of Science; and Ovid PsychInfo. Studies were included if they evaluated the relationship between SOC and oral health behaviors including tooth cleaning, fluoride usage, dietary habits, dental attendance, and smoking. We excluded studies that only assessed the relationship between oral health status and SOC without evaluating oral health behaviors. The New Castle Ottawa (NOS) quality assessment checklist was employed to evaluate the methodological quality of included studies. RESULTS: Thirty-nine potential papers met the preliminary selection criteria and following a full-text review, 9 papers were finally selected for this systematic review. Results provided by the included studies indicated different levels of association between SOC and oral health behaviors. The most frequent behaviors investigated were tooth brushing and dental attendance pattern. The impact of SOC on performing positive oral health behaviors, to some extent, was related to demographic and socio-economic factors. In addition, mothers' SOC influenced children's oral health practices. CONCLUSIONS: A more favorable oral health behavior was observed among those with a stronger SOC suggesting that the SOC can be a determinant of oral health-related behaviors including tooth brushing frequency, daily smoking, and dental attendance.
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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 | 0.019 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".