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Record W2158544761

Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness.

2006· article· en· W2158544761 on OpenAlexaff
Bernard C. K. Choi, Anita W. P. Pak

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsTransdisciplinarityMultidisciplinary approachTeamworkDisciplineInterdisciplinarityEngineering ethicsContext (archaeology)Management scienceSociologyMultidisciplinary teamKnowledge managementEpistemologyMedicinePolitical scienceSocial scienceComputer scienceEngineeringNursingGeography
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND/PURPOSE: Teamwork involving multiple disciplines is increasingly emphasized in health research, services, education and policy. The terms multidisciplinary, interdisciplinary and transdisciplinary are increasingly used in the literature, but are ambiguously defined and interchangeably used. This paper is the first of two in a series. It discusses the definitions, objectives, and evidence of effectiveness of such teamwork. METHODS: The paper is a literature review based on dictionaries, and Google and MEDLINE (1982-2006) searches. RESULTS: Multidisciplinarity draws on knowledge from different disciplines but stays within their boundaries. Interdisciplinarity analyzes, synthesizes and harmonizes links between disciplines into a coordinated and coherent whole. Transdisciplinarity integrates the natural, social and health sciences in a humanities context, and transcends their traditional boundaries. The objectives of multiple disciplinary approaches are to resolve real world or complex problems, to provide different perspectives on problems, to create comprehensive research questions, to develop concensus clinical definitions and guidelines, and to provide comprehensive health services. Multiple disciplinary teamwork has both benefits and drawbacks. CONCLUSION: The three terms refer to the involvement of multiple disciplines to varying degrees on the same continuum. The common words for multidisciplinary, interdisciplinary and transdisciplinary are additive, interactive, and holistic, respectively. With their own specific meanings, these terms should not be used interchangeably. The more general term "multiple disciplinary" is suggested for when the nature of involvement of multiple disciplines is unknown or unspecified. While multiple disciplinary teamwork is appropriate for complex problems, it is not always necessary in every single project.

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)0.158
metaresearch head score (Gemma)0.150
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.837

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1580.150
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0200.023
Science and technology studies0.0040.040
Scholarly communication0.0200.028
Open science0.0050.019
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.191
GPT teacher head0.460
Teacher spread0.269 · 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.

Study designTheoretical or conceptual
DomainMethods
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

Citations1,219
Published2006
Admission routes1
Has abstractyes

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