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Record W2752267739 · doi:10.2217/nmt-2017-0015

Health System Redesign Using Collective Impact: Implementation of the Behavioural Supports Ontario Initiative in Southwest Ontario

2017· article· en· W2752267739 on OpenAlexaffabout
Iris Gutmanis, Jennifer Speziale, Loretta M. Hillier, Elisabeth van Bussel, Julie Girard, K. M. Simpson

Bibliographic record

VenueNeurodegenerative Disease Management · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOntario Stroke NetworkHamilton Health SciencesSt Joseph's Health CareLawson Health Research Institute
Fundersnot available
KeywordsNeurocognitiveProcess managementMental healthScale (ratio)BusinessHealthcare systemCollective leadershipTheory of changePublic relationsService (business)NursingHealth carePsychologyKnowledge managementPolitical scienceMedicineCognitionMarketingComputer scienceSociologyPsychiatryGeography

Abstract

fetched live from OpenAlex

This paper describes how the Collective Impact framework facilitated the design, implementation and development of a quality improvement initiative aimed at changing the way healthcare is provided to older adults living with mental health, addictions, neurocognitive and behavioral issues in southwestern Ontario. By promoting a common agenda, shared measurement systems, mutually reinforcing activities, continuous communication and with leadership from a backbone organization, system-wide change occurred. Outcomes, operational/strategic, clinical, capacity enhancement and community support structures as well as challenges are discussed. Improved coordination with primary care will further support enhanced clinical activities and capacity development strategies. Large-scale, multisectoral change is possible when aligned with a collaborative, problem-solving framework that promotes the commitment of many service providers/agencies to a common agenda.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.423
GPT teacher head0.563
Teacher spread0.141 · 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 designObservational
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

Citations7
Published2017
Admission routes2
Has abstractyes

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