The creation and application of the Project Management Adherence Tool (PMAT) in understanding and advancing deflection programs and other community-based initiatives
Bibliographic record
Abstract
The opioid overdose crisis in the United States has given rise to innovative solutions to address behavioural health problems in communities. These complex initiatives vary greatly, making it difficult to understand what makes them successful and where their gaps and needs are, which impairs the ability to know how to best apply resources. This article examines Cordata’s Operation to Save Lives (O2SL) and Quick Response Team (QRT) National’s use of the Project Management Maturity Model (Maturity Model) to create an instrument, the Project Maturity Adherence Tool (PMAT) to better understand the state of complex, collaborative community-based programs that address substance use disorders (SUDs), opioid use disorders (OUDs), mental health disorders, community safety and well-being, and fatal overdoses in the United States. It represents a shift in the purpose of employing this model from industries like engineering, software development, and business to community-based initiatives to address behavioural health issues. In this setting, its primary purpose is to create structured communication within and between geographically dispersed, multifaceted initiatives that are united by common goals and funding streams but may have great diversity in how they operate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".