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Record W4392193200 · doi:10.1186/s12913-024-10697-7

Reducing wait times and avoiding unnecessary use of high-cost mental health services through a Rapid Access and Stabilization Program: protocol for a program evaluation study

2024· article· en· W4392193200 on OpenAlexafffundabout
Medard K. Adu, Raquel da Luz Dias, Gloria Obuobi-Donkor, Ngozi Ezeanozie, Sanjana Sridharan, Jason Morrison, Patryk Simon, Bryanne Taylor, Monica MacKinnon, Shiloh Gossen, Mahmoud A. Awara, Mattew D. White, Reham Shalaby, Belinda Agyapong, Ejemai Eboreime, JianLi Wang, Cindy Feng, Lori Wozney, Prosper Koto, Jordan Warford, Gail Tomblin Murphy, Vincent I. O. Agyapong

Bibliographic record

VenueBMC Health Services Research · 2024
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of AlbertaIzaak Walton Killam Health CentreNova Scotia Health AuthorityDalhousie University
FundersFaculty of Medicine, Dalhousie UniversityDalhousie University
KeywordsMental healthMedicineHealth administrationHealth informaticsHealth careNursing researchEmergency departmentPopulationPublic healthNursingHealth services researchFamily medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: 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. In light of this evidence there is a pressing need to provide accessible mental health services in flexible yet cost-effective ways. To further expand capacity and access to mental health care in the province, Nova Scotia Health has launched a novel mental health initiative for people in need of mental health care without requiring emergency department visits or hospitalization. This new service is referred to as the Rapid Access and Stabilization Program (RASP). 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. METHOD: This is a hypothesis-driven program evaluation study that employs a mixed methods approach. A within-subject comparison (pre- and post-evaluation study) 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 (cohort study) 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 health care partners' impressions. DISCUSSION: 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 both within-subject and between-subject comparisons. In addition, we anticipate that patients, healthcare providers and healthcare partners would express high levels of satisfaction with the new service. CONCLUSION: 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.

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)0.062
metaresearch head score (Gemma)0.046
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.062
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.046
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0040.005
Science and technology studies0.0060.003
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0610.011

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 head0.239
GPT teacher head0.566
Teacher spread0.327 · 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

Citations4
Published2024
Admission routes3
Has abstractyes

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