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Record W2584676113 · doi:10.1186/s13012-017-0542-7

Pathways for best practice diffusion: the structure of informal relationships in Canada’s long-term care sector

2017· article· en· W2584676113 on OpenAlexafffundabout
James W. Dearing, Amanda M. Beacom, Stephanie Chamberlain, Jingbo Meng, Whitney Berta, Janice Keefe, Janet E. Squires, Malcolm Doupe, Deanne Taylor, Robert Reid, Heather Cook, Greta G. Cummings, Jennifer Baumbusch, Jennifer Knopp‐Sihota, Peter Norton, Carole A. Estabrooks

Bibliographic record

VenueImplementation Science · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of CalgaryUniversity of British Columbia, Okanagan CampusOkanagan University CollegeInterior HealthUniversity of ManitobaManitoba HealthAthabasca UniversityUniversity of OttawaMount Saint Vincent UniversityUniversity of British ColumbiaPublic Health OntarioUniversity of TorontoUniversity of Alberta
FundersCanadian Institutes of Health ResearchCollege of Engineering, Michigan State UniversityAlberta InnovatesMichael Smith Health Research BCNova Scotia Health Research FoundationUniversity of AlbertaMount Saint Vincent UniversityResearch ManitobaMichigan State University
KeywordsOpinion leadershipPublic relationsHealth administrationHealth services researchKnowledge translationHealth careDiffusion of innovationsInterpersonal communicationBusinessPolitical sciencePsychologyMarketingSocial psychologyKnowledge managementLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Initiatives to accelerate the adoption and implementation of evidence-based practices benefit from an association with influential individuals and organizations. When opinion leaders advocate or adopt a best practice, others adopt too, resulting in diffusion. We sought to identify existing influence throughout Canada's long-term care sector and the extent to which informal advice-seeking relationships tie the sector together as a network. METHODS: We conducted a sociometric survey of senior leaders in 958 long-term care facilities operating in 11 of Canada's 13 provinces and territories. We used an integrated knowledge translation approach to involve knowledge users in planning and administering the survey and in analyzing and interpreting the results. Responses from 482 senior leaders generated the names of 794 individuals and 587 organizations as sources of advice for improving resident care in long-term care facilities. RESULTS: A single advice-seeking network appears to span the nation. Proximity exhibits a strong effect on network structure, with provincial inter-organizational networks having more connections and thus a denser structure than interpersonal networks. We found credible individuals and organizations within groups (opinion leaders and opinion-leading organizations) and individuals and organizations that function as weak ties across groups (boundary spanners and bridges) for all studied provinces and territories. A good deal of influence in the Canadian long-term care sector rests with professionals such as provincial health administrators not employed in long-term care facilities. CONCLUSIONS: The Canadian long-term care sector is tied together through informal advice-seeking relationships that have given rise to an emergent network structure. Knowledge of this structure and engagement with its opinion leaders and boundary spanners may provide a route for stimulating the adoption and effective implementation of best practices, improving resident care and strengthening the long-term care advice network. We conclude that informal relational pathways hold promise for helping to transform the Canadian long-term care sector.

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)0.010
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.456

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0120.010
Scholarly communication0.0090.003
Open science0.0020.009
Research integrity0.0010.001
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 head0.130
GPT teacher head0.481
Teacher spread0.350 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations32
Published2017
Admission routes3
Has abstractyes

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