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Record W1762738215 · doi:10.1080/14992020600944408

Providing an internet-based audiological counselling programme to new hearing aid users: A qualitative study

2006· article· en· W1762738215 on OpenAlexaff
Ariane Laplante-Lévesque, M. Kathleen Pichora‐Fuller, Jean‐Pierre Gagné

Bibliographic record

VenueInternational Journal of Audiology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of TorontoUniversité de Montréal
FundersWorld Health Organization
KeywordsAudiologistHearing aidThe InternetAudiologyQualitative researchOtorhinolaryngologyHearing lossPsychologyMedical educationMedicineComputer scienceWorld Wide WebPsychiatry

Abstract

fetched live from OpenAlex

People with an acquired hearing loss often have difficulty adjusting to a first hearing aid. Studies have shown that audiological counselling can facilitate adjustment to a first hearing aid. Because of its interactive nature, the internet could be a valuable tool to gain information about the experiences of the new hearing aid user and to address the needs for audiological counselling. An internet-based audiological counselling programme in the form of daily e-mails during the first month after the hearing aid fitting was offered to three new hearing aid users. The data, qualitative in nature, were comprised of the content of the e-mails and of in-depth interviews with the participants and their audiologist, and were analysed according to grounded theory. Overall, the internet-based audiological counselling programme provided rich descriptions of the experiences of the participants and reinforced positive adjustment behaviours experienced by them.

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)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0020.002
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 head0.114
GPT teacher head0.402
Teacher spread0.288 · 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 designQualitative
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

Citations67
Published2006
Admission routes1
Has abstractyes

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