Poetry Therapy, Disability, and Trauma Expression: A Therapeutic-Phenomenological Perspective
Bibliographic record
Abstract
This study aims to explore how individuals with disabilities express traumatic experiences through literary works from a therapeutic-phenomenological perspective. The research employs a qualitative-phenomenological method, with data collected from 45 literary works written by individuals with disabilities. The therapeutic process involved filtering, handling, and follow-up stages, involving 45 participants with disabilities. Data analysis was conducted through identification, classification, reduction, and exposition. The findings revealed varied themes: social criticism (35.5%), absurdism (17.7%), religion (13.3%), romanticism (4.4%), feminism (2.2%), and other themes (26.6%). Social criticism was the most dominant theme, followed by absurdism, religion, romanticism, feminism, and others. These works not only reflect emotional expression but also serve as a medium for critiquing discrimination and injustice experienced in society. The trauma expressed is primarily relational, such as social rejection and bullying, beyond just physical limitations. This study confirms that literary works are a vital means for individuals with disabilities to authentically voice their experiences and symbolically resist non-inclusive social systems. These findings aim to enrich interdisciplinary studies in literature, psychology, therapy, and disability studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".