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Record W2776290553 · doi:10.1080/14015439.2017.1414302

Inadequate vocal hygiene habits associated with the presence of self-reported voice symptoms in telemarketers

2017· article· en· W2776290553 on OpenAlexaff
Eduardo Fuentes–López, Adrián Fuente, Karem V. Contreras

Bibliographic record

VenueLogopedics Phoniatrics Vocology · 2017
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsHygienePsychologyPhonationAudiologyMedicinePathology

Abstract

fetched live from OpenAlex

Aim: The aim of this study is to determine possible associations between vocal hygiene habits and self-reported vocal symptoms in telemarketers.Patients and methods: A cross-sectional study that included 79 operators from call centres in Chile was carried out. Their vocal hygiene habits and self-reported symptoms were investigated using a validated and reliable questionnaire created for the purposes of this study.Results: Forty-five percent of telemarketers reported having one or more vocal symptoms. Among them, 16.46% reported that their voices tense up when talking and 10.13% needed to clear their throat to make their voices clearer. Five percent mentioned that they always talk without taking a break and 40.51% reported using their voices in noisy environments. The number of working hours per day and inadequate vocal hygiene habits were associated with the presence of self-reported symptoms. Additionally, an interaction between the use of the voice in noisy environments and not taking breaks during the day was observed. Finally, the frequency of inadequate vocal hygiene habits was associated with the number of symptoms reported.Conclusions: Using the voice in noisy environments and talking without taking breaks were both associated with the presence of specific vocal symptoms. This study provides some evidence about the interaction between these two inadequate vocal hygiene habits that potentiates vocal symptoms.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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 teacher head, 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

Citations19
Published2017
Admission routes1
Has abstractyes

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