Health Communication and Psychological Distress: Exploring the Language of Self-harm
Bibliographic record
Abstract
Abstract: This study explores adolescents’ accounts of self-harm with a view to elucidate the implications for health care practitioners seeking to administer care to teenagers in English. Drawing on a corpus of 1.6 million words from messages posted on a UK-hosted adolescent health Web site, analysis began by identifying a range of keywords relating to self-harm. The subsequent contextual examination of these keywords afforded a close description of the contributors’ experiences of self-harm and the factors that resulted in their self-injurious behaviours. A recurring theme was that of the habitual nature of self-harm, with the act being represented as a form of addiction over which they had little control. Self-harmers construct the phenomenon as particularly powerful, and the act is formulated as the only effective means of relief from emotional turmoil. If we are to increase parents and health professionals’ ability to respond to self-injury in the medium of English, close linguistic attention to individuals’ accounts of self-harm is valuable. Online health resources are also valuable means of eliciting concerns from distressed adolescents who are often reluctant to seek support from professionals face-to-face.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".