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Record W2801666595 · doi:10.1177/2331216518769789

Time of Day and Hearing Aid Adoption

2018· article· en· W2801666595 on OpenAlexaff
Gurjit Singh, Stefan Launer

Bibliographic record

VenueTrends in Hearing · 2018
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoToronto Metropolitan UniversityUniversity Health Network
Fundersnot available
KeywordsHearing aidAudiologyNoonRehabilitationMedicineCausality (physics)PsychologyPhysical therapy

Abstract

fetched live from OpenAlex

To date, there is little understanding of how contextual factors may influence the decisions individuals make regarding the adoption of options for hearing rehabilitation. This explorative retrospective study investigated whether hearing aid adoption and return rates are associated with the time of the day at which an appointment takes place. The study sample consisted of 24,842 patients experiencing their first audiology appointment. It was observed that hearing aid adoption was significantly associated with appointment times whereby lower hearing aid adoption rates were observed at noon and 4 p.m. It was also observed that hearing aid return rates were significantly associated with appointment times whereby lower return rates were observed at noon and 4 p.m. In light of the methodology employed in the study, it is not possible to unequivocally determine why time of day is associated with hearing aid adoption and return rates. Several possible explanations for the patterns of associations are discussed. In light of previous research observing that hunger lowers risk tolerance and glucose consumption increases risk tolerance, the results are consistent with an interpretation based on risk-aversion resulting from hunger. To establish causality between hunger and decision-making in audiology, additional research employing experimental methodologies are necessary.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.256

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 head0.067
GPT teacher head0.325
Teacher spread0.258 · 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 designBench or experimental
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

Citations13
Published2018
Admission routes1
Has abstractyes

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