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Record W292354831 · doi:10.1093/pch/15.9.583

Development of an integrated child health information system for children who are deaf or hard of hearing

2010· article· en· W292354831 on OpenAlexaff
Brenda T. Poon, Clyde Hertzman

Bibliographic record

VenuePaediatrics & Child Health · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of British ColumbiaLearning Partnership
Fundersnot available
KeywordsMultidisciplinary approachService (business)Service providerIntervention (counseling)Service delivery frameworkHearing lossProcess (computing)Service systemInformation systemPsychologyMedicineBusinessNursingComputer scienceSociologyEngineeringMarketingAudiology

Abstract

fetched live from OpenAlex

Young children who are deaf or hard of hearing typically participate in health and early intervention service structures involving multiple agencies and service providers, all of whom may be responsive to a child's and family's needs, but remain mutually distinct with minimal interdependence. Lack of coordination may result in fragmented service delivery and may be counterproductive to the provision of family centred services. Increasingly, advancements in technology, such as the development of integrated child health information systems, have facilitated greater coordination and integration of service delivery in multidisciplinary and multisite program contexts. In the present article, the process of developing an integrated child health information system for a new provincial early hearing detection and intervention program in British Columbia is described. Key considerations for system development included the following: the nature of the preprogram structure for information management and sharing; the need for modifications of the structure; and ways that the structure could be improved.

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.221
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
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 head0.019
GPT teacher head0.254
Teacher spread0.236 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2010
Admission routes1
Has abstractyes

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