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Social Cognitive Ontology and User Driven Healthcare

2009· book-chapter· en· W2205683799 on OpenAlexaff
Rakesh Biswas, Carmel M. Martin, Joachim P. Sturmberg, Kamalika Mukherji, Edwin Wen Huo Lee, Shashikiran Umakanth

Bibliographic record

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsNOSM University
Fundersnot available
KeywordsOntologyHealth carePremiseKnowledge managementMeaning (existential)CognitionKey (lock)FeelingProcess (computing)Computer sciencePsychologyEpistemologySocial psychologyPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

The chapter starts from the premise that illness and healthcare are predominantly social phenomena that shape the perspectives of key stakeholders of healthcare. It introduces readers to the concepts associated around the term ontology with particular reference to philosophical, social and computer ontology and teases out the relations between them. It proposes a synthesis of these concepts with the term ‘social cognitive ontological constructs’ (SCOCs). The chapter proceeds to explore the role of SCOCs in the generation of human emotions that are postulated to have to do more with cognition (knowledge) than affect (feelings). The authors propose a way forward to address emotional needs of patients and healthcare givers through informational feedback that is based on a conceptual framework incorporating SCOCs of key stakeholders. This would come about through recognizing the clinical encounter for what it is: a shared learning experience. The chapter proceeds to identify problems with the traditional development of top down medical knowledge and the need to break out of the well meaning but restrictive sub specialty approach. It uses the term de specialization to describe the process of breaking out of the traditional top down mold which may be achieved by collaborative learning not only across various medical specialties but also directly from the patient and her “other” caregivers. Finally it discusses current efforts in the medical landscape at bringing about this silent revolution in the form of a Web-based user driven healthcare. It also supplies a few details of the attempts made by the authors in a recent project trying to create electronic health records in a user driven manner beginning with the patient’s version of their perceived illness with data added on as the patient traverses his/her way through various levels of care beginning from the community to the tertiary care hospital. The data contained within these records may then be effectively and anonymously shared between different patients and health professionals who key in their own experiential information and find matching individual experiential information through text tagging in a Web 2.0 platform.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2009
Admission routes1
Has abstractyes

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