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Record W2810248945 · doi:10.1177/2055668318775313

Assessment of sex analysis in studies of technology-based interventions to alleviate caregiver burden among caregivers of persons with dementia

2018· article· en· W2810248945 on OpenAlexaff
Chen Xiong, Elizabeth Mansfield, Angela Colantonio

Bibliographic record

VenueJournal of Rehabilitation and Assistive Technologies Engineering · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsWomen's College HospitalToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsDementiaPsychological interventionInclusion (mineral)Intervention (counseling)Inclusion and exclusion criteriaGerontologyMedicineFamily caregiversClinical psychologyPsychologyPsychiatryAlternative medicineDiseasePathology

Abstract

fetched live from OpenAlex

BACKGROUND: With an increase in the number of family caregivers for persons with dementia, caregiver burden is a major concern. Defined as computer-based devices and programs, technology has been identified as an intervention to address this issue. However, to date, there is little consideration of sex differences among caregivers in the design and planning of these interventions. OBJECTIVE: To systematically review the literature on technology-based interventions for caregivers of persons with dementia and report the frequency and approaches of sex-based analysis. METHODS: The literature was systematically searched for reviews of technology-based interventions for caregivers of persons with dementia. All titles and abstracts of publications included in the retrieved reviews were screened using pre-determined inclusion and exclusion criteria. Full text articles that met the inclusion criteria were included for analysis. RESULTS: < 0.05) between male and female caregivers. CONCLUSIONS: There is currently a lack of (1) sex-based analyses, (2) inclusion of males and (3) provision of sex-specific information in studies of technology-based interventions for caregivers of persons with dementia.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.332
Teacher spread0.318 · 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 designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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Same venueJournal of Rehabilitation and Assistive Technologies EngineeringSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207