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Record W3016600786 · doi:10.1108/jica-11-2019-0048

Conceptual framework: factors enabling collaborative healthcare and social services integration

2020· article· en· W3016600786 on OpenAlexaff
Siu Mee Cheng, Cristina Catallo

Bibliographic record

VenueJournal of Integrated Care · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsKnowledge managementOriginalityConceptual frameworkAccountabilityHealth careBusinessConceptual modelCorporate governanceProcess managementIntegrated carePublic relationsComputer sciencePolitical scienceSociologyCreativity

Abstract

fetched live from OpenAlex

Purpose A conceptual framework for collaboratively based integrated health and social care (IHSC) integration is proposed to aid in understanding how to accomplish IHSC. Design/methodology/approach This model is based on extant literature of successfully IHSC initiatives. Findings The model aims to identify enabling integration factors that support collaborative integration efforts between healthcare and social services organizations. These factors include shared goals and vision, culture, leadership, team-based care, information sharing and communications, performance measurement and accountability agreements, and dedicated resources and financing. It also identifies factors that act as external influencers that can support or hinder integration efforts among collaborating organizations. These factors are geographic setting, funding models, governance structures, and public policies. These factors are intended to ensure that a realist lens is applied when trying to understand and explain IHSC. Originality/value This model is intended to provide a framework to support research, policy and implementation efforts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.044
GPT teacher head0.418
Teacher spread0.373 · 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.

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

Citations31
Published2020
Admission routes1
Has abstractyes

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