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Record W4394253344 · doi:10.6084/m9.figshare.13003345

The vicious cycle of functional neurological disorders: a synthesis of healthcare professionals’ views on working with patients with functional neurological disorder

2020· dataset· en· W4394253344 on OpenAlexaff
Caroline Barnett, Rebecca S. Davis, Claire Mitchell, Sarah Tyson

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsVirtuous circle and vicious circleHealth professionalsHealth careFunctional disorderPsychologyMedicinePhysical medicine and rehabilitationPsychiatryNeurosciencePolitical scienceEconomics

Abstract

fetched live from OpenAlex

The objective of this review was to synthesise studies which address the views of healthcare professionals (HCPs) towards patients with functional neurological disorder (FND). An interpretive systematised review was conducted. Seven databases were searched using a comprehensive search strategy (MEDLINE, EMBASE, AMED, CINAHL, PsychINFO, ProQuest Nursing and Allied Health, and Scopus). Qualitative studies and those using survey methods were included. An inductive approach to thematic analysis was used to identify concepts from the data and to synthesise the results. The views of 2769 HCPs were represented in 11 included articles. The overarching theme across the articles was uncertainty: about making the diagnosis of FND, about professional roles, and about optimum management. Fear was also a common theme: of saying the wrong thing, of offending patients, or of breaking the therapeutic relationship. If all HCPs felt uncertain about how to manage patients with FND and avoided them by passing them on to another discipline, then a “vicious cycle” is formed in which patients are passed from one professional to another but without receiving clear, honest information, or effective treatment. HCPs would benefit from increased training on FND and clear clinical pathways to alleviate feelings of uncertainty.Implications for rehabilitationEvidence-based or, at the very least, consensus-based multi-disciplinary care pathways for the assessment and treatment of patients with functional neurological disorder are required to improve equitability of services.Training packages for healthcare professionals need to be developed, evaluated and implemented in order to improve confidence of making and explaining the diagnosis and to reduce stigma of functional neurological disorders.Improved input for patients with functional neurological disorder is likely to occur if healthcare professionals are open, honest and use effective communication skills, both with their patients and fellow healthcare professionals. Evidence-based or, at the very least, consensus-based multi-disciplinary care pathways for the assessment and treatment of patients with functional neurological disorder are required to improve equitability of services. Training packages for healthcare professionals need to be developed, evaluated and implemented in order to improve confidence of making and explaining the diagnosis and to reduce stigma of functional neurological disorders. Improved input for patients with functional neurological disorder is likely to occur if healthcare professionals are open, honest and use effective communication skills, both with their patients and fellow healthcare professionals.

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 imitation

Not 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.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.080
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.080
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0120.011
Science and technology studies0.0020.003
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.270
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2020
Admission routes1
Has abstractyes

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