Exploring medical and educational systems and their impacts on mental health diagnosis and treatment
Bibliographic record
Abstract
Recent work focusing on the importance of neuroscience as it relates to educational principles has brought into light significant organizational and systems-based problems that impact the application of these prinicples among high school and college age students. These challenges, coupled with the ever increasing time demands and financial needs of health care providers in the United States has created an environment where schools are not adequately equipped to address mental health concerns and providers are limited in the time and resources available to treat in the office. Subsequently, ever increasing numbers of school aged children are being prescribed medications to treat symptoms that may be an artifact of the structure of the school day and the structure of the health care systems in the United States. This poster presentation seeks to present the argument of the need to address the aforementioned concerns, especially in light of the ever increasing mental health difficulties facing high school and college age children in the United States. It will also compare and contrast the systems in place in Canada and the United States to demonstrate strengths and weaknesses. Emphasis will be placed on diagnoses of ADHD, Anxiety, and Depression among high school and college aged students. This presentation will attempt to assimilite work from the Fulbright experience and subsequent related clinical experiences.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".