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Record W4312882307 · doi:10.2196/37569

A Stepped Health Services Intervention to Improve Care for Mental and Neurological Diseases: Protocol for a Prospective Cohort Trial

2022· article· en· W4312882307 on OpenAlexvenueno aff
Johannes Pollmanns, Karlheinz Großgarten, Julia K. Wolff, Hans-Dieter Nolting, Clarissa Graf, Frank Bergmann, Gereon Nelles

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)MedicineIntervention (counseling)Mental healthCohortProspective cohort studyPsychologyPsychiatryFamily medicinePhysical therapyGerontologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Mental and neurological disorders cause a large proportion of morbidity burden and require adequate health care structures. However, deficits in the German health care system like long waiting times for access to specialized care and a lack of coordination between health care providers lead to suboptimal quality of care and elevated health care costs. OBJECTIVE: To overcome these deficits, we implement and evaluate a unique stepped and coordinated model of care (the Neurologisch-psychiatrische und psychotherapeutische Versorgung [NPPV] program) for patients with mental and neurological diseases. METHODS: Patients included in the program receive an appropriate treatment according to medical needs in a multiprofessional network of ambulatory health care providers. The therapy is coordinated by a managing physician and complemented by additional therapy modules, such as group therapy, internet-based cognitive behavioral therapy, and a case management. Statutory health insurance (SHI) routine data and data from a longitudinal patient survey will be used to compare the program with regular care and evaluate SHI expenditures and patient-related outcomes. A health care provider survey will evaluate the quality of structure and processes and provider satisfaction. Finally, an analysis of ambulatory claims data and drug prescription data will be used to evaluate if health care providers follow a needs-led approach in therapy. Ethics approval for this trial was obtained from the ethics committee of the chamber of physicians in North Rhine (September 13, 2017, reference No. 2017287). RESULTS: Patient enrollment of NPPV ended in September 2021. Data analysis has been completed in 2022. The results of this study will be disseminated through scientific publications, academic conferences, and a publicly available report to the German Federal Joint Committee, which is expected to be available in the first half of 2023. CONCLUSIONS: The NPPV program is the first intervention to implement a stepped model of care for both mental and neurological diseases in Germany. The analysis of several data sources and a large sample size (more than 14,000 patients) enable a comprehensive evaluation of the NPPV program. TRIAL REGISTRATION: German Clinical Trials Register DRKS00022754; https://tinyurl.com/3mx9pz5z. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37569.

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)0.022
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.068
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.021
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0090.006
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0680.012

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 head0.114
GPT teacher head0.596
Teacher spread0.482 · 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 designNon-randomized trial
Domainnot available
GenreProtocol

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

Citations1
Published2022
Admission routes1
Has abstractyes

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