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Record W3130490215 · doi:10.1080/14992027.2021.1872805

Psychosocial outcome measures for conductive and mixed hearing loss treatment: An overview of the relevant literature

2021· review· en· W3130490215 on OpenAlexaff
Amberley Ostevik, Penny Hill-Feltham, Martin L. Johansson, Brian J. McKinnon, Peter Monksfield, Ravi Sockalingam, James R. Tysome, Tracy Wright, William Hodgetts

Bibliographic record

VenueInternational Journal of Audiology · 2021
Typereview
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychosocialAudiologyHearing lossConductive hearing lossOutcome (game theory)PsychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify the psychosocial assessments utilized with individuals with conductive and/or mixed hearing loss as part of a broader effort by the Auditory Rehabilitation Outcomes Network (AURONET) group to develop a core set of patient-centred outcome measures. DESIGN: A review of articles published between 2006 and 2016 was completed. Included studies had more than three adult participants, were available in English, and reported a psychosocial outcome from any treatment of mixed and/or conductive hearing loss. STUDY SAMPLE: Sixty-six articles from seven databases. RESULTS: Sixty-six articles met our inclusion/exclusion criteria. Within this set, 15 unique psychosocial or patient-reported outcome measures (PROs) were identified, with the Abbreviated Profile of Hearing Aid Benefit (APHAB) and Glasgow Benefit Inventory (GBI) being the most frequently dispensed. Five of the fifteen were only administered in one study. In-house questionnaires (IHQs) were reported in 19 articles. CONCLUSIONS: Only 66 (22%) of the 300 articles with outcomes contained a PRO. Some of the mostly frequently employed PROs (e.g., APHAB) were judged to include only social items and no psychological items. Lack of PRO standardization and the use of IHQs make psychosocial comparisons across treatments in this population difficult for patients, clinicians and stakeholders.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.279
GPT teacher head0.474
Teacher spread0.194 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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