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Record W2061244888 · doi:10.1159/000266237

Vocal Fatigue Among Teachers

2009· article· en· W2061244888 on OpenAlexaff
Christine Gotaas, Clark D. Starr

Bibliographic record

VenueFolia Phoniatrica et Logopaedica · 2009
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsAlberta Hospital Edmonton
Fundersnot available
KeywordsAnxietyPsychologyLoudnessAudiologyClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Recordings were made at the beginning and end of workdays of teachers who experience vocal fatigue (n = 22) and those who do not experience fatigue (n = 17). Those who experienced fatigue were recorded on days in which they did and did not fatigue. Both groups evaluated their vocal characteristics, each time they made a recording. Subsequently, a listener panel evaluated the same characteristics from the recordings. Both groups estimated the amount and characteristics of their talking time, completed a psychological evaluation and provided medical histories. The authors interpret the data obtained as indicating that the vocal characteristics of teachers who fatigue and those who do not fatigue are similar on days the former group does not fatigue and that the two groups are similar in the amount and loudness of their talking time, at work and at home. However, teachers who fatigue tend to spend more time in activities that appear to be vocally demanding and are more likely to perceive situations as being anxiety producing. Teachers who fatigue tend to be in good health, but have had more hearing problems and allergies than their colleagues and more of their family members have had voice problems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.017
GPT teacher head0.306
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations274
Published2009
Admission routes1
Has abstractyes

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