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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".