Occupational stress and sleep as adjuvant factors in the development of parafunctional oral habits and decreased of oral health
Bibliographic record
Abstract
Stress is an inherent biological response to various stressors, which, when experienced chronically, can lead to a disruption in the body’s homeostasis, resulting in pathophysiological changes. This raises the question of to what extent the stressful environment experienced by military police officers in the state of Rio de Janeiro, combined with poor sleep quality, influences the development of parafunctional habits and impacts their oral health. Military male police officers (211); 37.8 ± 5.5 years old, 86.6 ± 12.1 kg, overweight (27.7 ± 3.5 kg/m2), 11.9 ± 5.6 years of experience; were divided into two groups and were submitted a sociodemographic questionnaire and evaluated for the presence of occupational stress, sleep quality (SQ), mandibular function (MFIQ) and oral health self-perception (OHIP-14). They were diagnosed with poor sleep quality (59.5%/p = .023) and symptoms of occupational stress (34.6%); while psychological discomfort (p = .005) and the act of chewing hard food are the items that cause the greatest negative impact on oral health (OHIP-14) and jaw function (MFIQ). Self-perception of oral health can be directly correlated with occupational stress (p < .05) and poorer sleepers had higher values of functional mandibular impairment (p = .022). Oral health and mandibular function did not negatively affect these soldiers, who were able to carry out their work and social activities normally.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".