Charting the Path Forward: Development, Goals and Initiatives of the 2019 Infectious Diseases Society of America Strategic Plan
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".