“All in the head”: using storytelling to help patients who suffer from mental illness and break the stigma
Bibliographic record
Abstract
All in the Head": Using Storytelling to Help Patients Who Suffer from Mental Illness and Break the Stigma Imagine feeling lost and broken but having no one to lean on.Picture yourself needing help and wanting so desperately to be nurtured and cared for, to be held and told that everything will be okay, but the emotional pain and mental suffering are kept a secret because of shame and insecurity.Mental illness is a rising concern, and suicide is the second leading cause of death for Canadian youth ("Suicide in Canada").Moreover, because "mental disorders are a major risk factor for suicide," not receiving help can be dangerous, and the undeniable stigma surrounding mental illness is a barrier that prevents many individuals from seeking and receiving care (Too et al. 311).Patients have even showed up to emergency centers throughout Canada desperately begging for support and been turned away from the exact place that is meant to help.Unfortunately, providing information on the severity of mental health stigma in young people and the potential consequences of it is not sufficient to make a change, but using patients' personal experiences of mental illnesses as education tools can.The storytellers have the power to guide others, including healthcare professionals, to treat mental illness seriously and enhance peoples' empathy toward those who are sick, as right now, medical professionals' biases are not only emotionally hurting patients but can even threaten their lives.Being vulnerable and speaking up about a mental health problem is difficult, but those who are given the chance to reveal their struggles in a safe place can develop a sense of agency and begin to heal.Narrative
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".