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Record W2472877191 · doi:10.1097/aud.0000000000000292

Effort and Displeasure in People Who Are Hard of Hearing

2016· article· en· W2472877191 on OpenAlexaff
Mohan Matthen

Bibliographic record

VenueEar and Hearing · 2016
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsActive listeningPleasurePsychologyCognitive psychologyTask (project management)ArousalSocial psychologyCommunication

Abstract

fetched live from OpenAlex

Listening effort helps explain why people who are hard of hearing are prone to fatigue and social withdrawal. However, a one-factor model that cites only effort due to hardness of hearing is insufficient as there are many who lead happy lives despite their disability. This article explores other contributory factors, in particular motivational arousal and pleasure. The theory of rational motivational arousal predicts that some people forego listening comprehension because they believe it to be impossible and hence worth no effort at all. This is problematic. Why should the listening task be rated this way, given the availability of aids that reduce its difficulty? Two additional factors narrow the explanatory gap. First, we separate the listening task from the benefit derived as a consequence. The latter is temporally more distant, and is discounted as a result. The second factor is displeasure attributed to the listening task, which increases listening cost. Many who are hard of hearing enjoy social interaction. In such cases, the actual activity of listening is a benefit, not a cost. These people also reap the benefits of listening, but do not have to balance these against the displeasure of the task. It is suggested that if motivational harmony can be induced by training in somebody who is hard of hearing, then the obstacle to motivational arousal would be removed. This suggests a modified goal for health care professionals. Do not just teach those who are hard of hearing how to use hearing assistance devices. Teach them how to do so with pleasure and enjoyment.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.260
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.273
Teacher spread0.235 · 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

Citations48
Published2016
Admission routes1
Has abstractyes

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