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Record W1992633499 · doi:10.1097/aud.0b013e318157676d

Parents' Preferences for Services for Children with Hearing Loss: A Conjoint Analysis Study

2007· article· en· W1992633499 on OpenAlexafffundabout
Elizabeth M. Fitzpatrick, Doug Coyle, Andrée Durieux-Smith, Ian D. Graham, Douglas E Angus, Isabelle Gaboury

Bibliographic record

VenueEar and Hearing · 2007
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
FundersHospital for Sick Children
KeywordsConjoint analysisAudiologyHearing lossPsychologyPreferenceMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

In Brief Objective: Early identification of permanent childhood hearing loss through universal newborn hearing screening is rapidly becoming a standard of care. However, it is well recognized that hearing screening must be embedded within a comprehensive system of rehabilitation and parent support services. This study was undertaken with parents of young children with permanent hearing loss to examine their preferences for characteristics associated with intervention services. A secondary goal was to explore whether preferences may differ according to patient subgroups. Design: Conjoint analysis, a preference-based economic technique, was used to investigate parents' strength of preferences. A cross-sectional survey that consisted of hypothetical clinic scenarios was developed based on information from qualitative interviews with parents. The questionnaire was administered to parents receiving intervention services in the province of Ontario, Canada, shortly after the implementation of a universal hearing screening program. The sample was recruited from three different clinical programs. Results: A total of 48 of 75 respondents completed the questionnaire, a response rate of 64%. The participants varied by screening status of the child (25 screened, 23 not screened), type of device (23 hearing aids, 25 cochlear implants), and region. All five characteristics of care that were selected for inclusion in the survey were found to be statistically significant attributes of services: coordinated services, access to parent support, access to information, frequency of services, and location of services. Parents showed a preference for clinic-based rather than home-based services. Preferences toward once a week therapy services rather than services two to three times weekly were also found. In particular, parents valued service models that consisted of well-coordinated care with access to support from other parents. Differences in respondents according to hearing screening status (screened or unscreened), type of hearing device (hearing aid or cochlear implant), or region (Ottawa or Toronto) did not seem to affect parents' preferences for attributes of care. Conclusions: Conjoint analysis is a useful technique for quantifying parents' preferences for care. The values expressed by parents provide insights into the aspects of a service model that should receive consideration in the development of programs for young children with hearing loss and their families. Population-based infant hearing screening has received worldwide attention as an opportunity to improve outcomes for children with hearing loss. However, there is limited information available on how to maximize the potential opportunities provided by early detection of hearing loss. Using conjoint analysis, a stated preference technique from the field of marketing, this study explored parent preferences for service delivery after the identification of hearing loss. The results of the questionnaire demonstrated that, above all other characteristics of service models, parents valued coordinated services that were available through one agency or at least managed through one agency. Overall, the survey indicated that the preferred model was coordinated, clinic-based services with a range of audiology, speech and psychosocial services, access to parent support, and information available through the health service program.

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 score0.010
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.060
GPT teacher head0.320
Teacher spread0.260 · 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

Citations39
Published2007
Admission routes3
Has abstractyes

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