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Record W4392861085 · doi:10.1177/10497323241235882

From Promise to Practice: How Health Researchers Understand and Promote Transdisciplinary Collaboration

2024· article· en· W4392861085 on OpenAlexaff
Michael Lawless, Matthew Tieu, Mandy M. Archibald, Maria Alejandra Pinero de Plaza

Bibliographic record

VenueQualitative Health Research · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of Manitoba
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsMultidisciplinary approachTransdisciplinarityContext (archaeology)ExcellenceQualitative researchEngineering ethicsExperiential learningSociologyKnowledge managementPsychologyPedagogyPolitical scienceEngineeringSocial scienceComputer science

Abstract

fetched live from OpenAlex

There is an increasing emphasis on transdisciplinary research to address the complex challenges faced by health systems. However, research has not adequately explored how members of transdisciplinary research teams perceive, understand, and promote transdisciplinary collaboration. As such, there is a need to investigate collaborative behaviors, knowledge, and the impacts of transdisciplinary research. To address this gap, we conducted a longitudinal realist evaluation of transdisciplinary collaboration within a 5-year National Health and Medical Research Council-funded Center of Research Excellence in Transdisciplinary Frailty Research. The current study aimed to explore researchers' perceptions and promotion of transdisciplinary research specifically within the context of frailty research using qualitative methods. Participants described transdisciplinary research as a collaborative and integrative approach that involves individuals from various disciplines working together to tackle complex research problems. However, participants often used terms like interdisciplinary and multidisciplinary interchangeably, indicating that a shared understanding of transdisciplinary research is needed. Barriers to transdisciplinary collaboration included time constraints, geographical distance, and entrenched collaboration patterns. To overcome these challenges, participants suggested implementing strategies such as creating a shared vision and goals, establishing appropriate collaboration systems and structures, and role modeling collaborative behaviors, values, and attitudes. Our findings underscore the need for practical knowledge in developing transdisciplinary collaboration and leadership skills across different career stages. In the absence of formal training, sustained and immersive programs that connect researchers with peers, educators, and role models from various disciplines and provide experiential learning opportunities, may be valuable in fostering successful transdisciplinary collaboration.

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)0.349
metaresearch head score (Gemma)0.403
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.802

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3490.403
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0100.005
Science and technology studies0.0330.079
Scholarly communication0.0670.075
Open science0.0130.083
Research integrity0.0270.038
Insufficient payload (model declined to judge)0.0100.006

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 head0.712
GPT teacher head0.707
Teacher spread0.005 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
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

Citations19
Published2024
Admission routes1
Has abstractyes

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