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Record W2922470555 · doi:10.1080/14643154.2019.1589075

Examining cultural competence in pediatric hearing loss services: A survey

2019· article· en· W2922470555 on OpenAlexaffabout
Viviane Grandpierre, Flora Nassrallah, Beth K. Potter, Elizabeth M. Fitzpatrick, Roanne Thomas, Jenn Taylor, Lindsey Sikora

Bibliographic record

VenueDeafness & Education International · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsCultural competenceHearing lossCompetence (human resources)PsychologyPsychological interventionService providerMedical educationService delivery frameworkCultural diversityNursingMedicineService (business)PedagogySocial psychologyBusinessSociologyAudiology

Abstract

fetched live from OpenAlex

Efforts to improve cultural competence in pediatric hearing loss services should be informed by evidence of how culture can affect services, yet there is a paucity of research in the field of audiology. The aim of this study was to gain insight into practitioners’ experiences with offering early hearing loss services to minority culture families. Specifically, the objective was to examine possible barriers and facilitators to improving cultural competence in early hearing loss services by exploring the perspectives of the providers of care from across Canada. A survey, informed by our previous studies, was disseminated to practitioners providing hearing loss services. Responses to all survey questions were analyzed descriptively. Open-ended questions were analyzed with simple content analysis. Results indicate practitioners reported barriers at each stage of service delivery: during diagnoses, amplification discussions, language assessments, and interventions. Practitioners also described various strategies used to facilitate culturally competent care. Findings stemming from this study can be used to inform practitioners seeking to provide culturally competent care.

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)0.001
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 score0.020
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.383
Teacher spread0.316 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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