Shared language and communicating with adolescents and young adults with eating disorders
Bibliographic record
Abstract
Communication is one of the most important components of the work health care providers (HCP) do with adolescents and young adults (AYA). There are AYA with specific disorders or diseases that require particular attention to both the content of the language and the way in which HCP use this language when communicating with their patients. This is particularly true for AYA with eating disorders (ED). AYA with ED can experience or perceive language differently than we intend. The HCP may unintentionally make a comment or provide words of advice to an AYA struggling with an ED that can trigger or perpetuate the disorder. Developing a mutual understanding among HCP, trainees and caregivers of the content of the language and how to use this content more thoughtfully and empathetically is referred to as shared language (1). This means noticing, understanding, and being mindful of how the meaning of certain words may signify something very different to an AYA with an ED, and appreciating that the ED itself can alter the perception of the communication. Developing a shared language is essential to the therapeutic and trusting relationships.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".