“A Calm Arises”: An Anxious Adolescent’s Experience of Building Self-Compassion Through Aesthetic Reading
Bibliographic record
Abstract
In recent years, the Ontario Government (2013) has recognized that schools have a vital role in facilitating the prevention, intervention, and awareness of student mental wellness. Increased political attention to student mental health parallels studies that have found that 68.8% of the mental health problems in Canada have their onset in childhood and adolescence (Government of Canada, 2006). Of particular concern is anxiety since almost 5% of Canadians report experiencing threshold or subthreshold levels of Generalized Anxiety Disorder. Therefore, it is crucial that we find tools that can be used to promote mental wellness in anxious adolescents. Stories have great cultural importance because they help us explore abstract and difficult concepts and experiences. For this reason, literature provides a rich opportunity to promote mental wellness in anxious adolescents. In my study, I looked inwards at my own experiences as an anxious adolescent for insight into how reading literature can help adolescents understand and cope with their anxiety. Using autoethnography and literary anthropology as the methodologies for my study, I analyzed internal (autobiographical memories and reflections) and external (personal journals, social media posts, letters, and high school assignments) data sources from my adolescence (ages 14 to 18 years) and found that my aesthetic reading responses to literature helped me understand and cope with my anxiety by building my self-compassion. This study adds to current research investigating the relationship between anxiety and self-compassion by contributing a first-person exploration of the intersections between literacy experiences, adolescent anxiety, and self-compassion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| 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".