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Features and impacts of five multidisciplinary community-university research partnerships

2009· article· en· W2076195942 on OpenAlexafffundabout
Gillian King, Michelle Servais, Cheryl Forchuk, Heather Chalmers, Melissa Currie, Mary Law, Jacqueline Specht, Peter Rosenbaum, Teena Willoughby, Marilyn K. Kertoy

Bibliographic record

VenueHealth & Social Care in the Community · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster UniversityWestern UniversityThames Valley Children's CentreBrock UniversityChild and Family Research Institute
FundersCanadian Health Services Research Foundation
KeywordsMultidisciplinary approachGeneral partnershipScope (computer science)Public relationsChecklistDiversity (politics)RealmSample (material)PerceptionService (business)PsychologyBusinessPolitical scienceMarketingComputer science

Abstract

fetched live from OpenAlex

Despite the increasing number of multidisciplinary community-university research partnerships designed to address real-world issues, little is known about their nature. This article describes the features and impacts of five research partnerships addressing health or social service issues, which constituted a convenience sample from the province of Ontario, Canada. The article describes their characteristics, ways of operating, outputs, types of requests received from community members and mid-term impacts. Requests directed to partnerships were tracked over a 10-month period in 2003 to 2004, using a research contact checklist, and 174 community members later completed an impact questionnaire capturing perceptions of the impacts of the partnerships on personal knowledge and research skill development, organisational/group access to and use of information, and community and organisational development. The data indicated that partnerships had similar priorities and magnitudes of mid-term impacts, yet differed in the scope of their partnering, realm of intended influence and the number of mechanisms used to engage and communicate with target audiences. The partnerships produced different types of outputs and received different types of requests from community members. The findings inform researchers about partnership diversity and help to establish more realistic expectations about the magnitude of partnerships' impacts.

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)0.033
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0150.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.007
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 head0.750
GPT teacher head0.693
Teacher spread0.057 · 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 designQualitative
Domainnot available
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

Citations28
Published2009
Admission routes3
Has abstractyes

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