Quantifying Outcomes and Value: A Case Management Imperative
Bibliographic record
Abstract
Vivian Campagna, MSN, RN-BC, CCM, is the Chief Industry Relations Officer for the Commission for Case Manager Certification (CCMC), the first and largest nationally accredited organization that certifies case managers, and a former CCMC Commissioner. She has been active in case management for more than 25 years in independent practice as well as in acute care where she directed case management and social work departments. In her current position with CCMC, she draws on her expertise in the field and passion for certification to establish relationships with organizations that advocate for or promote the services of case management. Teresa Yancey, JD, RN, CCM, is a Commissioner of the CCMC and a Director of Affordability and Solutions for a Fortune 50 company. She is also licensed as an attorney in Georgia. Address correspondence to Vivian Campagna, MSN, RN-BC, CCM, Commission for Case Manager Certification, 1120 Route 73, Ste 200, Mount Laurel, NJ 08054 ([email protected]). The authors report no conflicts of interest.
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 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 | 0.467 | 0.597 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.009 | 0.066 |
| Scholarly communication | 0.041 | 0.060 |
| Open science | 0.012 | 0.025 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".