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Record W3162243328 · doi:10.1186/s43058-021-00152-7

Organizational supports for knowledge translation in paediatric health centres and research institutes: insights from a Canadian environmental scan

2021· article· en· W3162243328 on OpenAlexafffundabout
Stephanie Glegg, Andrea Ryce, K. J. Miller, Laura Nimmon, Anita Kothari, Liisa Holsti

Bibliographic record

VenueImplementation Science Communications · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsBC Children's HospitalWestern UniversitySunny Hill Health Centre for ChildrenUniversity of British Columbia
FundersCanadian Institutes of Health ResearchSunny Hill FoundationUniversity of British ColumbiaCanadian Child Health Clinician Scientist Program
KeywordsStaffingSnowball samplingKnowledge translationHealth careDescriptive statisticsBusinessMedical educationNursingPsychologyKnowledge managementPublic relationsMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Organizational supports are thought to help address wide-ranging barriers to evidence-informed health care (EIHC) and knowledge translation (KT). However, little is known about the nature of the resources and services that exist within paediatric health care and research settings across Canada to facilitate evidence use in health care delivery. This survey examined existing supports for EIHC/KT within these organizations to inform the design of similar EIHC/KT support programmes. METHODS: A national environmental scan was conducted using a bilingual online survey distributed to leaders at Canadian paediatric academic health science centres and their affiliated research institutes. Participants were invited through email, social media and webinar invitations and snowball sampling. Supports of interest included personnel, resources, services, organizational structures or processes, and partnerships or collaborations; barriers and successes were also probed. Data were compiled by site, reported using descriptive statistics, or grouped thematically. Supports were described using the AIMD (Aims, Ingredients, Mechanism, Delivery) framework. RESULTS: Thirty-one respondents from 17 sites across seven provinces represented a 49% site response rate. Eleven (65%) sites reported an on-site library with variable staffing and services. Ten (59%) sites reported a dedicated KT support unit or staff person. Supports ranged from education, resource development and consultation to protocol development, funded initiatives and collaborations. Organizations leveraged internal and external supports, with the majority also employing supports for clinical research integration. Supports perceived as most effective included personnel, targeted initiatives, leadership, interdepartmental expertise, external drivers and logistical support. Barriers included operational constraints, individual-level factors and lack of infrastructure. CONCLUSIONS: This first survey of organizational supports for EIHC/KT identified the range of supports in place in paediatric research and health care organizations across Canada. The diversity of supports reported across sites may reflect differences in resource capacity and objectives. Similarities in EIHC/KT and research integration supports suggest common infrastructure may be feasible. Moreover, stakeholder engagement in research was common, but not pervasive. Tailored support programmes can target multi-faceted barriers. Findings can inform the development, refinement and evaluation of EIHC/KT support programmes and guide the study of the effectiveness and sustainability of these 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)0.016
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.883

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.032
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.018
Science and technology studies0.0260.007
Scholarly communication0.0080.003
Open science0.0040.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.393
GPT teacher head0.601
Teacher spread0.208 · 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 designObservational
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

Citations5
Published2021
Admission routes3
Has abstractyes

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