Exploring Family Care Journeys to Inform Cognitive-Behavioral Therapy for Avoidant/Restrictive Food Intake Disorder and Somatic Symptom Disorders
Bibliographic record
Abstract
Avoidant/restrictive food intake disorder (ARFID) and gastrointestinal (GI)-related somatic symptom and related disorders (SSRDs) commonly co-occur, and both are associated with confusion in the process of accessing treatment. Furthermore, health professionals report low confidence in providing care for these conditions. Using a life history methodology, we explored the journeys of children and their parents with the diagnosis and treatment of ARFID and/or SSRDs and examined themes in barriers and facilitators to care. Six families with children (4 boys and 2 girls; 8–14 years old) with a diagnosis of ARFID and/or GI-related SSRD were recruited from a pediatric tertiary-level hospital. Interviews were conducted with four parents alone, and two parent-child dyads. Participants provided rich histories of the child’s health journeys with variation in the development of ARFID and GI-related SSRDs and subsequent management. Diagnostic uncertainty, the emotional impact of this journey on families, and systemic barriers to accessing treatment were themes of the healthcare narratives. Validating the emotional impacts of the healthcare journey and building trust may be helpful to address the diagnostic uncertainty that families experience. Strategies to support adaptation of cognitive-behavioral approaches for with children with complex ARFID and/or SSRDs are discussed.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".