Valuing patient perspectives in the context of eating disorders
Bibliographic record
Abstract
PURPOSE: This paper advocates for the inclusion of patient perspectives in the diagnosis and treatment of eating disorders (EDs) for ethical, epistemological, and pragmatic reasons. We build upon the ideas of a recent editorial published in this journal. Using EDs as their example, the authors argue against dominant DSM-oriented approaches in favor of an increased focus on understanding patients' subjective experiences. We argue that their analysis stops too soon for the development of practical-and actionable-insights into how to effect the integration of first-person and third-person accounts of EDs. METHODS: Contextual analysis was used to make the case for patient perspectives. RESULTS: We use anorexia nervosa (AN) as an example to demonstrate how the integration of patient manifestations and voices offers a promising methodology to improve patient diagnosis and treatment. We suggest that Acceptance and Commitment Therapy (ACT) can support patients with AN by reconciling their values with the values that arise from a clinician's duty of care. CONCLUSIONS: We conclude that there are no good scientific reasons to exclude first-person perspectives of EDs in psychiatry. LEVEL OF EVIDENCE: Level V: Opinions based on clinical experience.
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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 | 0.035 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 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".