Self-reported voice problems and contributing factors in a Francophone population of professional and student teachers and nurses
Bibliographic record
Abstract
Context: We explored the relation between work related factors, health practices, personality traits and stress-coping strategies and self-reported voice problems in a population of 354 student teachers (ST), 344 professional teachers (PT), 147 student nurses (SN) and 104 professional nurses (PN). Method: An online survey was conducted. Beside an anamnestic questionnaire to collect data about voice problems, work environment, health practices the VHI-10 was used to quantify voice symptoms, the Big Five-10 to explore personality traits, and the WCC-27 to explore stress coping strategies. Results: The prevalence of self-reported voice problems was significantly higher in ST as compared to SN (23% vs 14%, p = 0.025) and in PT versus PN (24% vs 12%, p = 0.08). VHI scores were significantly higher for subjects self-reporting a voice problem and significantly higher in the professional than the student groups, with PT with self-reported voice problems having the highest scores (Mean = 22.34 SD = 0.635) p<0.001. An ANOVA shows that 55% of the variance of the VHI scores is explained by the status of the subject (ST/PT/SN/PN), (F(3,945) = 382.156, p<0.001). A linear regression shows that 43% of the variance of the VHI scores was explained by Amount of private voice use, Conscientiousness, Extraversion and Emotion centered coping scores (F(4,830) = 159.201,p<0.001).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".