Integration of GVSU Biostatistics in the Mental Health Field
Bibliographic record
Abstract
PURPOSE: Professional Science Master’s in Biostatistics program requires an internship as part of the program requirements. The proposed poster showcases the completed internship projects from Community Mental Health at Ottawa County (CMH). Throughout the internship, we were able to apply statistical methods learned in the curriculum on real world data. The internship was beneficial for interns to assist CMH in continuing their pursuit of better evidence based decision- making. CHALLENGE: The challenges that we encountered were learning how to use lay terms to explain statistical concepts and results, and retrieving data from a database (Crystal Report) with which we were not familiar. EXPERIENCE: Data sets related to mental health, such as performance indicators and costs of running mental health programs, were cleaned, analyzed, interpreted, reported, and presented to leadership and social workers at CMH. OUTCOME: We learned how to effectively deliver statistical terms in lay terms, write detailed statistical reports, and prepare executive summaries that impacted decision-making for the organization. We also had many opportunities to learn various methods of delivering results using Excel, enabling those who were not familiar with statistical techniques to make better use of the information. IMPACT: The internship at CMH provided us the opportunity to interact with people who are not familiar with statistics and apply everything we learned from the curriculum to real world data. The mentorship we received helped guide us to explore data creatively and efficiently, challenges that we will respond to in future application of our skills.
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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 | 0.067 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.052 | 0.021 |
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".