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Record W2112182311 · doi:10.1503/cmaj.090661

Perspectives of clinician and biomedical scientists on interdisciplinary health research

2009· article· en· W2112182311 on OpenAlexafffundvenueabout
Suzanne Laberge, Mathieu Albert, Brian Hodges

Bibliographic record

VenueCanadian Medical Association Journal · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of TorontoUniversity Health NetworkUniversité de MontréalUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsViewpointsValue (mathematics)Variety (cybernetics)Engineering ethicsMedical educationMedicineHealth services researchPublic relationsPublic healthPolitical scienceComputer scienceNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Interdisciplinary health research is a priority of many funding agencies. We surveyed clinician and biomedical scientists about their views on the value and funding of interdisciplinary health research. METHODS: We conducted semistructured interviews with 31 biomedical and 30 clinician scientists. The scientists were selected from the 2000-2006 membership lists of peer-review committees of the Canadian Institutes of Health Research. We investigated respondents' perspectives on the assumption that collaboration across disciplines adds value to health research. We also investigated their perspectives on funding agencies' growing support of interdisciplinary research. RESULTS: The 61 respondents expressed a wide variety of perspectives on the value of interdisciplinary health research, ranging from full agreement (22) to complete disagreement (11) that it adds value; many presented qualified viewpoints (28). More than one-quarter viewed funding agencies' growing support of interdisciplinary research as appropriate. Most (44) felt that the level of support was unwarranted. Arguments included the belief that current support leads to the creation of artificial teams and that a top-down process of imposing interdisciplinary structures on teams constrains scientists' freedom. On both issues we found contrasting trends between the clinician and the biomedical scientists. INTERPRETATION: Despite having some positive views about the value of interdisciplinary research, scientists, especially biomedical scientists, expressed reservations about the growing support of interdisciplinary research.

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)0.118
metaresearch head score (Gemma)0.132
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.623

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1180.132
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0180.022
Scholarly communication0.0140.007
Open science0.0020.012
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.116
GPT teacher head0.514
Teacher spread0.398 · 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 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
Published2009
Admission routes4
Has abstractyes

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