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Record W4312086891 · doi:10.1002/alz.069162

Impact of a web‐based psychoeducational intervention on carer mental health quality of life: Results from a pragmatic randomized control trial

2022· article· en· W4312086891 on OpenAlexaffabout
Hannah M. O’Rourke, Jennifer Swindle, Sunita Ghosh, Dorothy Chacinski, Pamela Baxter, Shelley Peacock, Genevieve Thompson, Véronique Dubé, Jayna Holroyd‐Leduc, Cheryl Nekolaichuk, Carrie McAiney, Wendy Duggleby

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of WaterlooUniversity of CalgaryUniversity of ManitobaMcMaster UniversityUniversité de MontréalUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsSpouseLonelinessMental healthRandomized controlled trialQuality of life (healthcare)DementiaIntervention (counseling)MedicinePsychological interventionGerontologyPsychologyNursingPsychiatryDisease

Abstract

fetched live from OpenAlex

Abstract Background Carers of a person who is living with dementia in long‐term care experience transitions which can challenge their mental health. My Tools for Care‐In Care (MT4C‐In Care) is an online psychoeducational intervention that aims to offer informational, appraisal, and emotional support to assist carers to adapt to the transitions they experience. This presentation will report on the impact of using MT4C‐In Care on carer outcomes including mental health quality of life (primary outcome), and secondary outcomes of social support, hope, self‐efficacy, grief and loneliness. Method In a single blinded pragmatic randomized control trial, 234 Canadians participated from Alberta, Saskatchewan, Manitoba, and Ontario between February 2020 and October 2021. Carers were eligible if they were ≥18 years of age, had an email address and internet access, and provided physical, emotional or financial care to an adult ≥65 years of age who was living with dementia in a long‐term care home. The intervention group received access to MT4C‐In Care for 2 months; the educational control group received an Alzheimer Society information booklet. Outcomes were assessed using valid and reliable scales at baseline, 2 months and 4 months in telephone interviews. Generalized estimating equations were used to compare changes in the outcomes over time in the treatment and control groups, adjusting for clinically important covariates of gender, age, heart disease, caregiver relationship (spouse vs. non‐spouse) and outcome variables measured at baseline. Result The primary outcome, mental health quality of life (p = 0.035), improved in both groups over time, along with hope (p = 0.041) and grief (physical distress, p = 0.068). Comparing change in the intervention to the control group, treatment benefit was observed for secondary outcomes of grief (existential concerns; p = 0.095) and social support (total score p = 0.049 and family subscale p = 0.065), but not for the primary outcome of mental health quality of life (p = 0.93). Conclusion MT4C‐In Care demonstrates benefits for carers in terms of both the primary outcome (mental health quality of life), and secondary outcomes. In comparison to an active educational control group, MT4C‐In Care lessens grief and promotes social support, helping to explain how this intervention promotes carers’ mental health.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.066
GPT teacher head0.452
Teacher spread0.385 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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
Published2022
Admission routes2
Has abstractyes

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