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Record W3206867162 · doi:10.1097/phh.0000000000001451

Ten Common Structures and Processes of High-Performing Primary Care Practices

2021· article· en· W3206867162 on OpenAlexaff
Ann Nguyen, Margaret M. Paul, Donna Shelley, Stephanie L. Albert, Deborah J. Cohen, Pamela Bonsu, Tamar Wyte‐Lake, Saul Blecker, Carolyn A. Berry

Bibliographic record

VenueJournal of Public Health Management and Practice · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsNortel (Canada)
FundersNational Center for Advancing Translational Sciences
KeywordsOutreachPrimary careContext (archaeology)NursingProcess managementBest practiceSample (material)Knowledge managementMedicinePsychologyBusinessFamily medicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Structures (context of care delivery) and processes (actions aimed at delivery care) are posited to drive patient outcomes. Despite decades of primary care research, there remains a lack of evidence connecting specific structures/processes to patient outcomes to determine which of the numerous recommended structures/processes to prioritize for implementation. The objective of this study was to identify structures/processes most commonly present in high-performing primary care practices for chronic care management and prevention. We conducted key informant interviews with a national sample of 22 high-performing primary care practices. We identified the 10 most commonly present structures/processes in these practices, which largely enable 2 core functions: mobilizing staff to conduct patient outreach and helping practices avoid gaps in care. Given the costs of implementing and maintaining numerous structures/processes, our study provides a starting list for providers to prioritize and for researchers to investigate further for specific effects on patient outcomes.

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)0.009
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.006
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.115
GPT teacher head0.459
Teacher spread0.344 · 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 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
Published2021
Admission routes1
Has abstractyes

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