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Record W4384695657 · doi:10.17294/2330-0698.2061

Leveraging the Strength of Collaboration in Rapidly Changing Times: The 29th Annual Conference of the Health Care Systems Research Network

2023· article· en· W4384695657 on OpenAlexfundno aff
Michael A. Horberg, Suzanne Simons

Bibliographic record

VenueJournal of patient-centered research and reviews · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersKaiser Permanente Washington Health Research InstituteAurora Research InstituteKaiser Permanente
KeywordsDowntownTheme (computing)Healthcare systemHealth carePower (physics)Medical educationMedicinePolitical scienceComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

On February 21, 2023, the 29th annual conference of the Health Care Systems Research Network (HCSRN) kicked off at the Sheraton Downtown Denver with more than 320 participants from 20 HCSRN member institutions. Attendees gathered, in person, to reconnect and network during the 3-day conference, which featured the theme Leveraging the Power of the Network in Rapidly Changing Times. This paper highlights takeaways from the conference’s plenary sessions, panel discussions, and abstract presentations.

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)0.042
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0420.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.000
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 head0.622
GPT teacher head0.644
Teacher spread0.022 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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