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Record W4409351846 · doi:10.1016/j.jagp.2025.04.208

Exploring the Landscape of Intensive Outreach Services for Older Adults With Serious Mental Illness: A Scoping Review

2025· review· en· W4409351846 on OpenAlexaff
Claire Stanley, Andrew Namasivayam, Sarah Colman, Vicky Stergiopoulos

Bibliographic record

VenueAmerican Journal of Geriatric Psychiatry · 2025
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsOutreachMental illnessGerontologyMental healthMedicinePsychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Older adults living with mental illness, particularly those with serious mental illness (SMI), represent a vulnerable and underserved patient population. Deinstitutionalization laid the groundwork for intensive outreach services for this population, yet little attention was paid to the unique needs of older individuals with SMI. We conducted a scoping literature review to map the landscape of intensive outreach services developed for older adults living with SMI. We specifically focused on peer-reviewed literature. EMBASE, MEDLINE, PsycINFO and CINAHL databases were searched for pertinent literature between 1990 and 2023. Fourteen studies were selected for inclusion from 2,952 articles screened. Most studies were descriptive (N = 7). There were three randomized controlled trials (N = 3), three quasi-experimental studies (N = 3) and one (N = 1) pre-experimental study. All programs had a multidisciplinary component with a wide range of allied health clinicians. Six programs were adapted from the assertive community treatment model. Three programs included medical specialists. Only seven studies focused on examining treatment outcomes or efficacy, though there was notable variability in outcome measures analyzed. While intensive outreach services for older adults with SMI show promise, this scoping review highlights the paucity of research in this area. Future research rigorously evaluating models of care, with emphasis on fidelity, cost-effectiveness and patient outcomes, will be essential to inform service delivery to this population.

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)0.015
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0180.018
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.338
Teacher spread0.313 · 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 designSystematic review
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

Citations1
Published2025
Admission routes1
Has abstractyes

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