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Record W4400151486 · doi:10.1111/jep.14056

Realist process evaluation of the knowledge translation programme of a patient‐oriented research network

2024· article· en· W4400151486 on OpenAlexafffundabout
Sakiko Yamaguchi, Alix Zerbo, Roberta Cardoso, Mayada Elsabbagh, Aryeh Gitterman, Stephanie Glegg, Miriam González, Connie Putterman, Jonathan A. Weiss, Keiko Shikako‐Thomas

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre for Addiction and Mental HealthUniversity of ManitobaBC Children's HospitalToronto Metropolitan UniversityMontreal Neurological Institute and HospitalYork UniversityUniversity of British ColumbiaMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsGeneral partnershipKnowledge translationKnowledge managementKnowledge sharingDocumentationProcess (computing)Context (archaeology)Transformative learningAdaptation (eye)Presentation (obstetrics)Process managementComputer scienceSociologyPsychologyBusinessMedicinePedagogy

Abstract

fetched live from OpenAlex

RATIONALE: The Knowledge Translation (KT) Programme of a pan-Canadian strategic patient-oriented research network focused on brain-based developmental disabilities aimed to mobilize knowledge relevant to the network members. The programme also promotes and studies integrated Knowledge Translation (iKT) approaches involving different interested parties, such as researchers, patient-partners and decision-makers, in all parts of the knowledge creation process. AIMS AND OBJECTIVES: The objective of this study is to advance research programme evaluation methods through a realist evaluation of the process of implementing iKT activities. METHODS: Realist process evaluation included: (1) development of initial programme theories (using the partnership synergy theory); (2) data collection and analysis; (3) synthesis and refinement of theories through engagement with literature; and (4) presentation of findings in context-mechanism-outcome (C-M-O) configurations. A range of project documentation records were reviewed for analysis, and three co-leads, a programme coordinator, and a senior research associate were consulted to contextualize the implementation process of relevant KT activities. RESULTS: Based on the developed C-M-O configurations, we identified five key mechanisms of generating synergy in the iKT processes: (1) Visible shared leadership that embodies what iKT looks like; (2) Researchers' readiness for iKT; (3) Adaptation and flexible allocation of resources to emerging needs; (4) Power sharing to create practical and creative knowledge; and (5) Collective voice for potential transformative impacts at the policy level. CONCLUSIONS: The current realist evaluation demonstrated how partnerships between researchers, patient-partners and other interested parties can synergistically generate new ways of thinking among all interested parties, actionable strategies to integrate users in research, and solutions to disseminate knowledge. In particular, we identified a pivotal role for patient-partners to act as equal decision-maker helps building and maintaining partnerships and consolidating KT strategies.

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)0.378
metaresearch head score (Gemma)0.386
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.622
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3780.386
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0070.008
Scholarly communication0.0090.007
Open science0.0060.013
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0120.002

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 head0.816
GPT teacher head0.712
Teacher spread0.104 · 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
DomainEvaluation
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

Citations2
Published2024
Admission routes3
Has abstractyes

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