Exploring the educational experiences of students living with early psychosis
Bibliographic record
Abstract
Contemporary conversations in education centered on supporting and creating inclusive, accepting learning environments for students facing learning and mental health challenges have neglected the experiences of individuals who live with early psychosis (Jones & Brown, 2012). Research focused on the academic functioning of students with psychosis have noted low academic achievement, and high dropout rates (Morgan et al., 2012; Laurence, Rousseau, Foriter & Mottard, 2016; Goulding, Chien & Compton, 2010). This presentation will examine the preliminary findings of a narrative inquiry and document analysis study, which explores the experiences of up to three students who live with psychosis. The first phase of the study will have each primary participant gather documents and complete a two-week diary that describes their everyday experiences and perceptions of inclusion and support within their school communities. Sharing and listening to the lived experiences of these students will provide teachers, administrators, and policy makers with the insight into the unique experiences of students who live with early psychosis, as well as begin a more inclusive discussion about mental health in education that includes the experiences of these students.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".