Education, interrupted: an autoethnographic exploration of barriers to accessing higher education for a student with psychiatric diagnoses
Bibliographic record
Abstract
In this thesis, I set out to answer a single overarching research question: in what ways does having a mental illness affect options regarding higher education? Given the prevalence of serious mental illness (SMI) in both the United States and Canada, and its frequent emergence during the late teens, it is both urgent and essential to identify barriers to reaching postsecondary education for students with SMI. Situating the work within a poststructuralist theoretical framework, I conducted autoethnographic research into my experiences of attempting to re-access postsecondary education after my initial attempt was derailed due to schizoaffective disorder. The autoethnographic writing—both method and product—incorporated analysis that considers the individual experience in the context of the university and society at large and how the discourse made available by the cultural context impacts the achieved identity of a student living with SMI. I produced new autoethnographic writing; analyzed excerpts from previous creative writing addressing my experience with schizoaffective disorder; and engaged with memoir by writers who have accessed higher education while living with SMI. The findings showed both psychological barriers, such as destabilized sense of self, and financial barriers, such as loss of scholarship opportunities, to accessing higher education for students with psychiatric diagnoses.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".