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Record W2275420898 · doi:10.1155/2016/5926303

Improving System Integration: The Art and Science of Engaging Small Community Practices in Health System Innovation

2016· article· en· W2275420898 on OpenAlexafffund
Pauline Pariser, Laura Pus, Ian Stanaitis, Howard Abrams, Noah Ivers, G. Ross Baker, Elizabeth Lockhart, Gillian Hawker

Bibliographic record

VenueInternational Journal of Family Medicine · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsToronto General HospitalWomen's College HospitalUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchUniversity of TorontoOntario Ministry of Health and Long-Term Care
KeywordsMedicineData scienceEngineering ethicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

UNLABELLED: This paper focuses on successful engagement strategies in recruiting and retaining primary care physicians (PCPs) in a quality improvement project, as perceived by family physicians in small practices. Sustained physician engagement is critical for quality improvement (QI) aiming to enhance health system integration. Although there is ample literature on engaging physicians in hospital or team-based practice, few reports describe factors influencing engagement of community-based providers practicing with limited administrative support. The PCPs we describe participated in SCOPE: Seamless Care Optimizing the Patient Experience, a QI project designed to support their care of complex patients and reduce both emergency department (ED) visits and inpatient admissions. SCOPE outcome measures will inform subsequent papers. All the 30 participating PCPs completed surveys assessing perceptions regarding the importance of specific engagement strategies. Project team acknowledgement that primary care is challenging and new access to patient resources were the most important factors in generating initial interest in SCOPE. The opportunity to improve patient care via integration with other providers was most important in their commitment to participate, and a positive experience with project personnel was most important in their continued engagement. Our experience suggests that such providers respond well to personalized, repeated, and targeted engagement strategies.

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)0.017
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.209
GPT teacher head0.488
Teacher spread0.279 · 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 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

Citations14
Published2016
Admission routes2
Has abstractyes

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