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Record W4390200699 · doi:10.1002/alz.079885

Associations between support service usage and social connectedness in those with lived experience of rare dementias

2023· article· en· W4390200699 on OpenAlexaff
Emilie Brotherhood, Olivia K Wood, Claire Waddington, Oliver S Hayes, Emma Harding, Céline El Baou, Millie van der Byl Williams, Rebecca E Street, Nikki Zimmermann, Zoë Hoare, Paul M. Camic, Rhiannon Tudor Edwards, Roberta McKee‐Jackson, Joshua Stott, Mary Pat Sullivan, Gill Windle, Sebastian J. Crutch

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsNipissing University
Fundersnot available
KeywordsSocial connectednessSocial supportLonelinessPsychologySocial network (sociolinguistics)DementiaGerontologySocial mediaClinical psychologyMedicineDiseaseSocial psychologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Background Disease‐specific and general support is available for individuals experiencing rare [genetic and/or non‐memory‐led] dementia, broadly categorised into: professionally‐led [e.g. interacting with specialist support teams/resources] or socially‐oriented [e.g. large/small groups, buddying, online fora, and social media] offers. Membership of and engagement with different social networks may help to maintain emotional wellbeing in people living with/alongside dementia. Understanding the relationship between support service use and social connectedness, at different disease and life stages, will enable healthcare professionals to make evidence‐based individualised recommendations for support. Method 207 participants either living with (PLWD N = 51) or alongside a rare dementia (Caregiver N = 156) provided responses via videoconference and online survey about support usage, and social connectedness. Support usage was characterised by (a) range of usage (indicated by number of different support formats accessed at least once); (b) frequency of usage rated using 4‐point Likert scales relating to 11 different types of support available. Level of social connectedness was ascertained through standardised scales: Social Network Index (SNI); De Jong Gierveld Loneliness Scale (6‐item), and Lubben Social Network Scale of social engagement (LSNS‐6). Associations between support usage and connectedness were examined separately for all/socially‐oriented support using multiple linear regressions covarying for group (PLWD/caregiver), gender, severity and age. Result Range of support usage was significantly associated with greater social engagement (LSNS‐6: 0.071[0.003,0.139]), greater social network size (SNI:0.035[0.001,0.068]) and fewer networks (SNI: ‐0.486[‐0.947,‐0.026]). Both frequency of all‐support‐type usage and range of socially‐oriented support access was additionally associated with greater social engagement (LSNS‐6: 0.195[0.022,0.369] and 0.044[0.001,0.086] respectively). Usage across all and socially‐oriented support types was significantly associated with group (PLWD>Carers: all‐types frequency = 2.102[0.104,4.101]; social‐only frequency = 1.620[0.242,2.997]), and greater disease severity (years since diagnosis: all types frequency = 0.361[0.047,0.674]; social‐only frequency = 0.354[0.138,0.570] and social‐only range = 0.124[0.046,0.201]). Usage across all support was significantly associated with younger membership (Frequency = ‐0.125[‐0.220,‐0.030]; Range = ‐0.051[‐0.088,‐0.013]). Conclusion Preliminary analysis indicates a relationship between range and frequency of support usage and social connectedness. The role of individual factors in and complexity of relationship between support usage and social connectedness underlines the need for co‐ordinated person‐centred provision of information‐ and socially‐based services.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.293
GPT teacher head0.433
Teacher spread0.139 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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