Self-reported voice difficulties in educational professionals during COVID-19 in Quebec: a cross-sectional mixed-methods study
Bibliographic record
Abstract
Background: The health measures imposed by COVID-19 on workplaces created adverse communication settings. Our cross-sectional study aimed to document the nature and severity of the vocal difficulties experienced by educational professionals a few weeks after the implementation of health measures in schools and early childhood settings in Quebec, Canada while teaching in class. Methods: To this end, we conducted a self-report survey containing nine close-ended questions and one open-ended question regarding self-reported vocal difficulties and the implementation of health measures. The survey was answered by 194 educational professionals in October 2020. Results: Since the introduction of the health measures, respondents reported often or always: having difficulty making themselves heard (66.5%), needing to strain their voice (68.1%), having throat pain after work (38.1%), and being concerned about their vocal health (25.2%). 35.6% perceived that their voice changed moderately or a lot and 75.3% did not feel equipped to take care of their vocal health. Fisher's exact tests revealed the difficulties overall were more present in women (p < 0.05). Discussion: The qualitative analysis of open-ended question answers shows a circular process at play, where the vocal responses to the COVID-19-induced communication barriers contribute to creating more problematic communication settings, thus increasing the challenges for vocal health. Better equipping the professionals to take care of their vocal health by developing resources in their professional settings to help them face vocal challenges in both every day as well as extreme situations, should be a priority.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".