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Record W2990211736 · doi:10.1002/lary.28351

Interdisciplinary integration of nursing and psychiatry (INaP) for the treatment of dizziness

2019· review· en· W2990211736 on OpenAlexaffabout
Philip Gerretsen, Parita Shah, Anastasia Logotheti, Mohamed Attia, Thushanthi Balakumar, Shaleen Sulway, Paul J. Ranalli, Wanda A. Dillon, David D. Pothier, John Rutka

Bibliographic record

VenueThe Laryngoscope · 2019
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsToronto General HospitalUniversity of TorontoCentre for Addiction and Mental HealthUniversity Health Network
Fundersnot available
KeywordsPsychoeducationPsychosocialMedicineChronic careIntervention (counseling)Integrated careNursingHealth carePsychiatryChronic diseaseIntensive care medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The traditional medical care model of "assess and refer" requires revamping to address the multifaceted needs of patients with chronic dizziness and imbalance by adopting an interdisciplinary approach to care that integrates nursing and psychiatry (INaP). We aim to present a novel interdisciplinary approach that incorporates INaP in the care of patients with chronic dizziness and imbalance. METHODS: Presentation of an interdisciplinary model of care that incorporates INaP provided at the Toronto General Hospital in Toronto, Canada. RESULTS: Interdisciplinary care incorporating INaP, which includes the provision of support from an interdisciplinary health care team (ie, neurotologist, neurologist, psychiatrist, physiotherapist, and nurse clinician), psychoeducation about the interaction between chronic dizziness and psychiatric comorbidities, and ongoing access to medical and psychosocial assessment and intervention, addresses the physical and emotional aspects of patients' experience with chronic dizziness. CONCLUSIONS: The novel comprehensive interdisciplinary approach incorporating INaP may be more effective than interdisciplinary care without INaP in improving clinical outcomes in patients with chronic dizziness. In the subsequent study, we present data comparing patients treated for chronic dizziness and imbalance with and without the integration of INaP in an interdisciplinary setting. LEVEL OF EVIDENCE: 5 Laryngoscope, 130:1792-1799, 2020.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.115
GPT teacher head0.411
Teacher spread0.296 · 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 designOther design
Domainnot available
GenreReview

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

Citations7
Published2019
Admission routes2
Has abstractyes

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