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Record W2971784637 · doi:10.5430/jha.v8n5p34

A conceptual model for physician-system integration: A scoping review

2019· review· en· W2971784637 on OpenAlexvenueno aff
Ann Nguyen, Suzanne Wood, Christopher E. Johnson, William L. Dowling

Bibliographic record

VenueJournal of Hospital Administration · 2019
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCategorizationOrder (exchange)Transaction costConceptual modelHealth careHealthcare systemConceptual frameworkKnowledge managementConceptual blendingDatabase transactionOrganizational theoryPsychologyManagement scienceBusinessComputer scienceSociologyEngineeringPolitical scienceManagementEconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

Despite the widespread pursuit of physician-health system integration, the evidence for factors affecting successful integration is uncertain and inconclusive. We sought to identify and categorize the organizational factors in the current landscape of physician-health system integration. We conducted a scoping review of the empirical literature on this topic, first surveying the theoretical perspectives that have been used in past studies in order to determine how theory has been used to explain and predict changing integration strategies over time. Second, we extracted factors that have been used to define the environment, physician group, hospital, care coordination, and health system success. From the 29 eligible articles, bargaining-market power theory and transaction cost theory were the predominant theories applied. We identified 48 organizational factors that comprise the landscape of physician-system integration. Our findings cumulated in a conceptual model that may help health care executives, policymakers, and researchers more effectively address the complexities of integration.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.608
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.196
GPT teacher head0.388
Teacher spread0.192 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2019
Admission routes1
Has abstractyes

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