Interdisciplinary integration of nursing and psychiatry (INaP) improves dizziness‐related disability
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The traditional medical care model of "assess and refer" in a sequential fashion fails to recognize the complexities that arise due to overlapping physical and psychiatric comorbidities experienced by patients with chronic dizziness or imbalance, thus resulting in inadequate treatment outcomes. We aimed to evaluate the impact of a novel interdisciplinary approach to care that integrates nursing and psychiatry (INaP) on dizziness-related disability. STUDY DESIGN: Retrospective cohort study. METHODS: We compared the change in clinical assessment scores (i.e., Dizziness Handicap Inventory [DHI], Dizziness Catastrophizing Scale) at approximately 8 months follow-up between those who did (INaP+) and did not receive INaP (INaP-). Data from 229 patients with dizziness or imbalance referred to an interdisciplinary neurotology clinic in Toronto, Ontario, Canada were acquired from August 2012 to December 2016 and January 2011 to December 2013 for the INaP+ and INaP- groups, respectively. RESULTS: A mean group difference in the percentage change in DHI scores was found, with greater reductions in dizziness-related disability in the INaP+ group (n = 121) versus the INaP- group (n = 108). This remained significant after controlling for age, gender, baseline illness severity, and duration between baseline and follow-up visits. CONCLUSIONS: The novel interdisciplinary approach of incorporating INaP appears to be more effective than interdisciplinary care without INaP in reducing dizziness-related disability in patients with chronic dizziness or imbalance. Clinical settings should consider the addition of INaP to achieve better patient outcomes. Future studies are required to test the hypothesis that INaP is more efficient and cost-effective than the traditional model of care. LEVEL OF EVIDENCE: 3 Laryngoscope, 130:1800-1804, 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".