MétaCan
Menu
Back to cohort
Record W2769010536 · doi:10.1044/persp2.sig17.83

Community-Based Hearing Rehabilitation: Implementation and Outcome Evaluation

2017· article· en· W2769010536 on OpenAlexaff
Spoorthi Thammaiah, Vinaya Manchaiah, Vijayalakshmi Easwar, Rajalakshmi Krishna, Bradley McPherson

Bibliographic record

VenuePerspectives of the ASHA Special Interest Groups · 2017
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsRehabilitationCommunity-based rehabilitationIntervention (counseling)Work (physics)Process (computing)Independent livingMedical educationNursingMedicineComputer scienceGerontologyEngineeringPhysical therapy

Abstract

fetched live from OpenAlex

Community-based rehabilitation (CBR) is a program designed to support persons with disabilities living in remote and rural areas. CBR primarily aims to provide required rehabilitation services to financially deprived persons with disabilities living in communities with limited access to such services. Today, globally, many organizations adhere to this approach to serve the rehabilitation needs of various disabilities. Recently, the CBR approach has started gaining interest in the rehabilitation sector concerned with hearing disabilities. This paper provides a general framework for the implementation and outcome evaluation of community-based hearing rehabilitation (CBHR) programs. The entire process is discussed with support of an existing CBHR program in India implemented by a non-governmental organization (NGO), Audiology India (AI). In addition, the paper highlights several of the challenges involved in such work and also explores future directions for CBHR-based intervention programs.

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)0.045
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.003
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.166
GPT teacher head0.422
Teacher spread0.256 · 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 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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePerspectives of the ASHA Special Interest GroupsSame topicHearing Loss and RehabilitationFrench-language works237,207