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Record W2973226214 · doi:10.1186/s12877-019-1249-1

Time to move? Factors associated with burden of care among informal caregivers of cognitively impaired older people facing housing decisions: secondary analysis of a cluster randomized trial

2019· article· en· W2973226214 on OpenAlexafffundabout
Alexandrine Boucher, Julie Haesebaert, Adriana Freitas, Rhéda Adekpedjou, Marjolaine Landry, Henriette Bourassa, Dawn Stacey, Jordie Croteau, Painchaud-Guérard Geneviève, France Légaré

Bibliographic record

VenueBMC Geriatrics · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCentres Intégré Universitaires de Santé et de Services SociauxOttawa HospitalCARE CanadaUniversité du Québec à Trois-RivièresUniversity of OttawaUniversité Laval
FundersNetworks of Centres of Excellence of CanadaCanadian Frailty NetworkMinistère de la SantéGovernment of CanadaMinistère de la Santé et des Services sociaux
KeywordsCaregiver burdenMedicineFamily caregiversGerontologyRandomized controlled trialRegretMultivariate analysisMultilevel modelCognitionDementiaPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Making health-related decisions about loved ones with cognitive impairment may contribute to caregiver burden of care. We sought to explore factors associated with burden of care among informal caregivers who had made housing decisions on behalf of a cognitively impaired older person. METHODS: We conducted a secondary analysis within a cluster randomized trial (cRT) conducted in 16 publicly-funded home care service points across the Province of Quebec. The cRT assessed the impact of training home care teams in interprofessional shared decision making (IP-SDM). We assessed burden of care with the Zarit Burden Interview (ZBI) scale. We adapted Pallett's framework to inform our data analysis. This framework posits that factors influencing burden of care among caregivers fall within four domains: (a) characteristics of the caregiver, (b) characteristics of the cognitively impaired older person, (c) characteristics of the relationship between the caregiver and the cognitively impaired older person, and (d) the caregiver's perception of their social support resources. We computed the ZBI score and performed multilevel linear regression modelling. RESULTS: Among 296 caregivers included in the dataset, the mean ZBI score was 29.8 (SD = 17.5) out of 88. The typical participant was 62.6 years old (SD = 11.7), female (74.7%), and caring for a mother or father (61.2%). Using multivariate analysis, factors significantly associated with caregiver burden mapped onto: caregiver characteristics (caregivers with higher burden were female, experienced higher decision regret and decisional conflict, preferred that their loved one move into the caregiver's home, into a private nursing home or a mixed private-public nursing home, and had made the decision more recently); relationship characteristics (spouses and children experienced higher burden); and caregiver's perception of social support resources (caregivers who perceived that a joint decision making process had occurred had higher burden). CONCLUSION: In line with the proposed framework used, we found that caregiver characteristics, relationship characteristics and caregiver's perception of social support resources were associated with burden of care. Our results will help design interventions to prevent and/or reduce caregivers' burden of care. TRIAL REGISTRATION: NCT02244359 . Date of registration: September 18, 2014.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.020
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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 teacher head, 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

Citations20
Published2019
Admission routes3
Has abstractyes

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