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Record W2895494882 · doi:10.1177/2331216518803215

Hearing, Emotion, Amplification, Research, and Training Workshop: Current Understanding of Hearing Loss and Emotion Perception and Priorities for Future Research

2018· review· en· W2895494882 on OpenAlexaff
Erin M. Picou, Gurjit Singh, Huiwen Goy, Frank Russo, Louise Hickson, Andrew J. Oxenham, Gabrielle H. Buono, Todd A. Ricketts, Stefan Launer

Bibliographic record

VenueTrends in Hearing · 2018
Typereview
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsPerceptionPsychologyHearing lossAffect (linguistics)PsychosocialRehabilitationEmotion perceptionAudiologyHearing aidCognitive psychologyMedicinePsychotherapistCommunicationNeuroscience

Abstract

fetched live from OpenAlex

The question of how hearing loss and hearing rehabilitation affect patients' momentary emotional experiences is one that has received little attention but has considerable potential to affect patients' psychosocial function. This article is a product from the Hearing, Emotion, Amplification, Research, and Training workshop, which was convened to develop a consensus document describing research on emotion perception relevant for hearing research. This article outlines conceptual frameworks for the investigation of emotion in hearing research; available subjective, objective, neurophysiologic, and peripheral physiologic data acquisition research methods; the effects of age and hearing loss on emotion perception; potential rehabilitation strategies; priorities for future research; and implications for clinical audiologic rehabilitation. More broadly, this article aims to increase awareness about emotion perception research in audiology and to stimulate additional research on the topic.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.648
GPT teacher head0.524
Teacher spread0.123 · 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 designNot applicable
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

Citations50
Published2018
Admission routes1
Has abstractyes

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