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Record W3203569068 · doi:10.1016/j.ebr.2021.100486

Changing the culture of care for children and adolescents with functional neurological disorder

2021· article· en· W3203569068 on OpenAlexaff
Kasia Kozlowska, Tyson Sawchuk, Jeff L. Waugh, Helene Helgeland, Janet Baker, Stephen Scher, Aaron D. Fobian

Bibliographic record

VenueEpilepsy & Behavior Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsEmpathyPsychological interventionMultidisciplinary approachReferralHealth careMedicineConversion disorderCompassionPsychiatryPsychologyNursing

Abstract

fetched live from OpenAlex

As members of a multidisciplinary team of professionals who treat children and adolescents with functional neurological (conversion) disorder (FND), we highlight the pressing need to develop an FND-informed culture of care that takes into account recent advances in our understanding of this group of patients. Stories of clinical encounters in health care settings from around the world—told by children and adolescents with FND, their parents, and health professionals—portray an outdated culture of care characterized by iatrogenic stigma, erosion of empathy and compassion within the clinician-patient relationship, and a lack of understanding of FND and its complex neurobiology. After a brief exploration of the outdated culture, we share our counterstories: how we and our colleagues have worked, and continue to work, to create an FND-informed culture in the health systems where we practice. We discuss the therapeutic use of child-friendly language. We also discuss a range of structural, educational, and process interventions that can be used to promote FND-informed beliefs and attitudes, FND-informed clinician-patient encounters, and FND-informed referral processes, treatment pathways, and therapeutic interventions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.253
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2021
Admission routes1
Has abstractyes

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