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Record W7117305992 · doi:10.1002/alz70858_101283

Best Practices for Non‐Pharmacological Treatments for Older Adults in the Geriatric Mental Health Outpatient Services (GMHOS) at the Centre for Addiction and Mental Health (CAMH)

2025· article· en· W7117305992 on OpenAlexaffabout
Senaya Karunarathne, Kimberly J. Martin, Dewi Clark, Ann‐Sylvia Brooker, Mackenzie Hilton, Jordanne Holland

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsBest practiceFacilitatorMental healthPsychological interventionAddictionService (business)Evidence-based practiceMental health service

Abstract

fetched live from OpenAlex

BACKGROUND: The Geriatric Mental Health Outpatient Services (GMHOS) at the Centre for Addiction and Mental Health (CAMH) in Toronto, Ontario, deliver evidence-based care to older adults with complex mental health conditions. GMHOS currently offers limited group programming tailored to the unique needs of its four outpatient clinics: Neuropsychiatry, Memory, Late Life Anxiety and Mood Disorders and General Geriatrics. Existing therapies, such as "Learning the Ropes" and "Goal Management Training," address cognitive and emotional needs but leave significant gaps for conditions like anxiety, depression, and dementia. Challenges related to resources and accessibility further constrain scalability. This quality improvement project aims to (1) gather insights from current GMHOS program facilitators regarding usability, satisfaction, effectiveness, and areas for improvement, and (2) conduct a literature review of academic research to identify evidence-based, nonpharmacological interventions delivered in outpatient settings that can enhance mental health outcomes for geriatric patients. METHOD: A mixed-methods approach was employed. Structured questionnaires were administered to GMHOS group facilitators to collect feedback on current programming using validated recovery-oriented tools, including the American Association of Community Psychiatrist Recovery-Oriented Service Evaluation (AACP-ROSE) and the Consumer Recovery Outcomes System (CROS). A comprehensive literature search was also conducted by a research librarian using PubMed and PsycINFO databases to identify evidence-based nonpharmacological therapies for outpatient geriatric mental health care. RESULT: Facilitators identified strengths in current programs, such as reminiscence therapy and art-based interventions, in promoting engagement and emotional well-being. However, they identified gaps in addressing cultural diversity, scalability, and accessibility. Suggested improvements included participant-driven activities, structured goal-setting, and enhanced staff training. Preliminary findings from the literature review identified therapies such as Yoga-Cognitive Behavioral Therapy (Y-CBT), Cognitive Stimulation Therapy (CST), mindfulness-based interventions, and life review therapy as effective for addressing conditions like anxiety, depression, and dementia. CONCLUSION: Facilitator feedback and evidence from the literature emphasize the importance of tailoring interventions to participant needs while fostering scalable and inclusive approaches. Integrating evidence-based therapies like Y-CBT and CST alongside enhancements to existing programs can address critical service gaps in GMHOS. Future steps include piloting new therapies, evaluating their long-term impacts, and ensuring sustained improvement in outcomes for geriatric outpatients.

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)0.024
metaresearch head score (Gemma)0.068
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: Methods · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.068
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.073
GPT teacher head0.420
Teacher spread0.347 · 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
GenreMethods

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

Citations0
Published2025
Admission routes2
Has abstractyes

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