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Record W2980600751 · doi:10.1093/cid/ciz1040

Charting the Path Forward: Development, Goals and Initiatives of the 2019 Infectious Diseases Society of America Strategic Plan

2019· review· en· W2980600751 on OpenAlexaff
Cynthia L. Sears, Thomas M. File, Barbara D. Alexander, Daniel P. McQuillen, Ann Macintyre, Upton Allen, Jonathan Colasanti, Javeed Siddiqui, Kelly R. Reveles, Chris Busky

Bibliographic record

VenueClinical Infectious Diseases · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsHospital for Sick Children
FundersInfectious Diseases Society of America
KeywordsStrategic planningBusinessPlan (archaeology)Process managementWorkforceProcess (computing)BenchmarkingHealth carePublic relationsKnowledge managementPolitical scienceMarketingComputer scienceGeography

Abstract

fetched live from OpenAlex

In October 2018, the Infectious Diseases Society of America (IDSA) Board of Directors (BOD) decided to develop a 2019 IDSA Strategic Plan. The IDSA BOD has invested in strategic planning at regular intervals as part of an ongoing process to review and to renew the vision and direction of IDSA. Herein, the 2018-2019 strategic planning process and outcomes are described. The 2019 IDSA Strategic Plan presents 4 key initiatives: (1) optimize the development, dissemination, and adoption of timely and relevant ID guidance and guidelines that improve the outcomes of clinical care; (2) quantify, communicate, and advocate for the value of ID physicians to increase professional fulfillment and compensation; (3) facilitate the growth and development of the ID workforce to meet emerging scientific, clinical, and leadership needs; and (4) develop and position a new tool to serve as the leading US benchmark to measure and drive national progress on antimicrobial resistance. The BOD looks forward to developing, implementing, assessing, and advancing the 2019 IDSA Strategic Plan working with member volunteers, Society partners, and IDSA staff.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.814
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.059
GPT teacher head0.352
Teacher spread0.293 · 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 designNot applicable
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

Citations16
Published2019
Admission routes1
Has abstractyes

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