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Record W4401921863 · doi:10.1192/j.eurpsy.2024.665

Health Outcomes and Health Services Utilization Evaluation Protocol: Assessing the Impact of the Nova Scotia Rapid Access and Stabilization Program

2024· article· en· W4401921863 on OpenAlexaffabout
Medard Kofi Adu, Raquel da Luz Dias, Gloria Obuobi-Donkor, Ngozi Ezeanozie, Shankar Sridharan, John M. Morrison, Patricia Simon, Brian Taylor, M. MacKinnon, Shiloh Gossen, Belinda Agyapong, Lori Wozney, Vincent I. O. Agyapong

Bibliographic record

VenueEuropean Psychiatry · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of AlbertaNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsNova scotiaNova (rocket)Protocol (science)MedicineEnvironmental healthGeographyEngineeringAlternative medicineAeronautics

Abstract

fetched live from OpenAlex

Introduction Emergency psychiatric care, unplanned hospital admissions, and inpatient health care are the costliest forms of mental health care. According to Statistics Canada (2018), almost 18% (5.3 million) of Canadians reported needing mental health support. However, just above half of this figure (56.2%) have reported their needs were fully met. To further expand capacity and access to mental health care in the province, Nova Scotia Health has launched a novel mental health initiative, the Rapid Access, and Stabilization Program (RASP). Objectives This study evaluates the effectiveness and impact of the RASP on high-cost health services utilization (e.g. ED visits, mobile crisis visits, and inpatient treatments) and related costs. It also assesses healthcare partners’ (e.g. healthcare providers, policymakers, community leaders) perceptions and patient experiences and satisfaction with the program and identifies sociodemographic characteristics, psychological conditions, recovery, well-being, and risk measures in the assisted population. Methods This is a hypothesis-driven program evaluation study that employs a mixed methods approach. A within-subject comparison will examine health services utilization data from patients attending RASP, one year before and one year after their psychiatry assessment at the program. A controlled between-subject comparison will use historical data from a control population will examine whether possible changes in high-cost health services utilization are associated with the intervention (RASP). The primary analysis involves extracting secondary data from provincial information systems, electronic medical records, and regular self-reported clinical assessments. Additionally, a qualitative sub-study will examine patient experience and satisfaction, and examine health care partners’ impressions. Results The results for the primary, secondary, and qualitative outcome measures to be available within 6 months of study completion. We expect that RASP evaluation findings will demonstrate a minimum 10% reduction in high-cost health services utilization and corresponding 10% cost savings, and also a reduction in the wait times for patient consultations with psychiatrists to less than 30 calendar days. In addition, we anticipate that patients, healthcare providers, and healthcare partners would express high levels of satisfaction with the new service. Conclusions This study will demonstrate the results of the Mental Health and Addictions Program (MHAP) efforts to provide stepped-care, particularly community-based support, to individuals with mental illnesses. Results will provide new insights into a novel community-based approach to mental health service delivery and contribute to knowledge on how to implement mental health programs across varying contexts. Disclosure of Interest None Declared

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)0.037
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.968
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.037
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.004
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0240.004

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 head0.170
GPT teacher head0.577
Teacher spread0.407 · 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 designNot applicable
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
Published2024
Admission routes2
Has abstractyes

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