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Record W2896348666 · doi:10.1016/j.jalz.2018.06.3003

O5‐02‐03: USING A PERSON‐CENTRED KE NETWORK TO IMPROVE THE LIVES OF THOSE EXPERIENCING DEMENTIA

2018· article· en· W2896348666 on OpenAlexaffabout
Jillian K. McConnell, Dallas Seitz

Bibliographic record

VenueAlzheimer s & Dementia · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsQueen's UniversityInstitute for Clinical Evaluative SciencesOntario Brain Institute
Fundersnot available
KeywordsKnowledge translationKnowledge managementDementiaKnowledge sharingContext (archaeology)PsychologyResource (disambiguation)Psychological interventionKnowledge transferQuality (philosophy)Action (physics)CognitionMedical educationBusinessNursingComputer scienceMedicine

Abstract

fetched live from OpenAlex

As a collaborative partner to various stakeholders, brainXchange is a critical component to the implementation of Knowledge Exchange (KE) in Canada, where we are aiming to synthesize and facilitate the exchange of new knowledge and information and precipitate new research questions in the field of dementia and cognitive impairment. brainXchange is a free, virtual network that promotes and champions knowledge utilization through multiple KE platforms, supporting the needs of people wanting to change practice; facilitating quick and easy access to knowledge for continuous quality improvement; stimulating, supporting and sharing innovations, building and strengthening collaborative partnerships between stakeholders and fostering links to create new knowledge and understanding. brainXchange recognizes the importance of an evidence-informed approach to building KE platforms and exchanges. The Knowledge Transfer Cycle (Sullivan et al., 2014) and the Knowledge to Action Framework (Graham, 2006) are used within the context of the PARiHS Framework (Kitson et al., 2008). These knowledge translation models provide the theoretical foundation for the successful KE interventions brainXchange provides through our: Resource Centre, Communities of Practice, Knowledge Dissemination Series, Forums and our skilled Knowledge Brokers. brainXchange has leveraged our network to move knowledge into action and help those diagnosed with dementia and other cognitive impairments, care partners and providers. Critical to the success of the KE strategies and the resultant knowledge translation activities brainXchange fosters, is a diverse group of members which includes policy makers, researchers, clinicians and persons with lived experience. KE between and within these sectors fosters person-centred, evidence-informed capacity enhancement and continuous quality improvement as demonstrated by our: 7500 Members; 20 knowledge dissemination events per year; publications: Shifting Focus, Pain Matters, BSO Capacity Roadmap; Online Resource Centre that comprises of over 4,000 links/documents, over 80 topic areas and over 200 archived webinars. brainXchange is leading/engaged in 18 Communities of Practice and supports 300 members active in 16 collaboration spaces. brainXchange continues to innovate, develop and support collaborative partnerships with Alzheimer Society of Canada, Canadian Consortium on Neurodegeneration in Aging, Canadian Coalition of Seniors Mental Health, Health Quality Ontario, MS Society of Canada, Behavioural Supports Ontario and others.

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)0.008
metaresearch head score (Gemma)0.012
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: none
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0050.005
Open science0.0020.014
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0230.005

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 head0.241
GPT teacher head0.408
Teacher spread0.167 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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