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Record W1626881430 · doi:10.3233/wor-2010-0961

Evaluating auditory perception and communication demands required to carry out work tasks and complimentary hearing resources and skills for older workers with hearing loss

2010· review· en· W1626881430 on OpenAlexaffabout
Madeleine Jennings, Lynn Shaw, H. Hodgins, D.A. Kuchar, L. Poost-Foroosh Bataghva

Bibliographic record

VenueWork · 2010
Typereview
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsHearing lossPerceptionWorkforceWork (physics)Applied psychologyRehabilitationPsychologyAudiologyComputer scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

For older workers with acquired hearing loss, this loss as well as the changing nature of work and the workforce, may lead to difficulties and disadvantages in obtaining and maintaining employment. Currently there are very few instruments that can assist workplaces, employers and workers to prepare for older workers with hearing loss or with the evaluation of auditory perception demands of work, especially those relevant to communication, and safety sensitive workplaces that require high levels of communication. This paper introduces key theoretical considerations that informed the development of a new framework, The Audiologic Ergonomic (AE) Framework to guide audiologists, work rehabilitation professionals and workers in developing tools to support the identification and evaluation of auditory perception demands in the workplace, the challenges to communication and the subsequent productivity and safety in the performance of work duties by older workers with hearing loss. The theoretical concepts underpinning this framework are discussed along with next steps in developing tools such as the Canadian Hearing Demands Tool (C-HearD Tool) in advancing approaches to evaluate auditory perception and communication demands in the workplace.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.064
GPT teacher head0.396
Teacher spread0.332 · 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.

Study designOther design
Domainnot available
GenreReview

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
Published2010
Admission routes2
Has abstractyes

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