Michigan Veterans Community Action Teams: Report On The Survey Of Veterans Service Providers
Bibliographic record
Abstract
The Michigan Veterans Community Action Teams (MIVCAT) project is a collaborative community model created by the Altarum to enhance the delivery of services from public, private, and nonprofit organizations to Veterans and their family members. The MIVCAT project was introduced in Michigan by the Michigan Veterans Affairs Agency (MVAA) in August 2013, with pilots in two of Michigan's ten Prosperity Regions – Detroit Metro Region 10, comprising Macomb, Oakland, and Wayne counties; and West Michigan Region 4, consisting of Allegan, Barry, Ionia, Kent, Lake, Mason, Mecosta, Montcalm, Muskegon, Newaygo, Oceana, Osceola, and Ottawa counties.To better discern the needs of Veterans and the services available to them, Altarum gathered information through several channels. Altarum conducted a community assessment that included interviews with key regional leaders, focus groups with Veterans, a survey of Veterans, and a survey of service providers working with Veterans. This report summarizes the survey of service providers.This survey was conducted between February and April 2014 using a web-based survey instrument. In both regions combined, 189 service providers (116 in Detroit Metro and 73 in West Michigan) from 151 organizations (93 in Detroit Metro and 58 in West Michigan) responded to the survey. Following are the key findings.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".