Holistically addressing motivation and maladaptive traits in anorexia nervosa: Impact on prognosis and treatment outcomes
Bibliographic record
Abstract
Anorexia Nervosa (AN) is a serious psychiatric disorder, characterized by restriction of energy intake, low body weight, intense fear of weight gain, and a disturbance in body weight self-perception. Severe and Enduring AN (SE-AN) is a long-lasting (typically 5-7 or more years and marked by several unsuccessful treatment attempts) form of AN. Traditional treatments, centering on weight restoration and core eating pathology, may be part of the reason rates of treatment dropout are high and long-term outcomes are poor, particularly in SE-AN. For SE-AN patients, who have a past marked by failed traditional treatment attempts, multidimensional treatments, addressing motivation to change and maladaptive traits, may improve a range of patient outcomes outside of eating-related symptoms, such as quality of life and interpersonal functioning.The objective of this narrative review is to briefly examine motivation-related factors (e.g., hope and readiness to change), experiential avoidance, perfectionism, and obsessive-compulsiveness, and the impact of treatment approaches incorporating these individual characteristics on various patient outcomes. In conclusion, a holistic, multidimensional, person-centred recovery approach that accounts for (a) illness severity/ chronicity, (b) individual traits, and (c) motivational factors (with a secondary focus on weight gain/eating pathology), could improve quality of life outcomes, particularly in SE-AN. Additionally, integrating patient perspectives, insights, and values into developing/testing novel person-centred interventions is paramount in order to holistically address the underlying biopsychosocial causes and perpetuating factors of AN, and to better understand the trajectory of chronicity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".