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Record W2042874316 · doi:10.1186/1471-2318-11-50

Reducing depression in older home care clients: design of a prospective study of a nurse-led interprofessional mental health promotion intervention

2011· article· en· W2042874316 on OpenAlexafffundabout
Maureen Markle‐Reid, Carrie McAiney, Dorothy Forbes, Lehana Thabane, Maggie Gibson, Jeffrey S. Hoch, Gina Browne, Thomas H. Peirce, Barbara Busing

Bibliographic record

VenueBMC Geriatrics · 2011
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcMaster UniversityParkwood InstituteSt. Joseph’s Healthcare HamiltonHome and Community Care Support ServicesUniversity of AlbertaUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsMedicineDepression (economics)Mental healthHospital Anxiety and Depression ScaleIntervention (counseling)Quality of life (healthcare)AnxietyPromotion (chess)Collaborative CareNursingRehabilitationFamily medicineGerontologyPsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Very little research has been conducted in the area of depression among older home care clients using personal support services. These older adults are particularly vulnerable to depression because of decreased cognition, comorbid chronic conditions, functional limitations, lack of social support, and reduced access to health services. To date, research has focused on collaborative, nurse-led depression care programs among older adults in primary care settings. Optimal management of depression among older home care clients is not currently known. The objective of this study is to evaluate the feasibility, acceptability and effectiveness of a 6-month nurse-led, interprofessional mental health promotion intervention aimed at older home care clients with depressive symptoms using personal support services. METHODS/DESIGN: This one-group pre-test post-test study aims to recruit a total of 250 long-stay (> 60 days) home care clients, 70 years or older, with depressive symptoms who are receiving personal support services through a home care program in Ontario, Canada. The nurse-led intervention is a multi-faceted 6-month program led by a Registered Nurse that involves regular home visits, monthly case conferences, and evidence-based assessment and management of depression using an interprofessional approach. The primary outcome is the change in severity of depressive symptoms from baseline to 6 months using the Centre for Epidemiological Studies in Depression Scale. Secondary outcomes include changes in the prevalence of depressive symptoms and anxiety, health-related quality of life, cognitive function, and the rate and appropriateness of depression treatment from baseline to 12 months. Changes in the costs of use of health services will be assessed from a societal perspective. Descriptive and qualitative data will be collected to examine the feasibility and acceptability of the intervention and identify barriers and facilitators to implementation. DISCUSSION: Data collection began in May 2010 and is expected to be completed by July 2012. A collaborative nurse-led strategy may provide a feasible, acceptable and effective means for improving the health of older home care clients by improving the prevention, recognition, and management of depression in this vulnerable population. The challenges involved in designing a practical, transferable and sustainable nurse-led intervention in home care are also discussed. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01407926.

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)0.012
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.006
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.392
Teacher spread0.337 · 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

Citations17
Published2011
Admission routes3
Has abstractyes

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