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Record W2900409648 · doi:10.1093/geroni/igy023.3125

DOES LONELINESS CAUSE INCREASED HEALTH AND SOCIAL CARE SERVICE USE BY OLDER PEOPLE? AN EVALUATION OF THE EVIDENCE

2018· article· en· W2900409648 on OpenAlexaboutno aff
Christina Victor

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessAttendanceMedicineService (business)Older peopleUCLA Loneliness ScaleSocial supportHealth careEmergency departmentGerontologyNursingPsychologyPsychiatryBusiness

Abstract

fetched live from OpenAlex

Loneliness in later life in the UK is generating a moral panic because of the consequences for the health and social care system of the increased demand generated by lonely older people. Loneliness for older people has been linked with increased use of primary care services, attendance at accident and emergency units, admission to acute hospitals and admissions to care homes. A comprehensive literature review identified 9 papers that reported service use for both lonely and not lonely people aged 50+ in Canada, the USA, Sweden, Singapore and Britain. Studies looked at the use of primary care, emergency department, hospital in-patient admissions and nursing home admissions and were generally small scale, use self-reported service use outcomes and are cross-sectional in design. Limitations in our current evidence base which do not support the extravagant claims made for the consequences of loneliness, in terms of service use, and subsequent costs.

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)0.032
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.112
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.0080.007
Science and technology studies0.0010.003
Scholarly communication0.0050.004
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.097
GPT teacher head0.408
Teacher spread0.312 · 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 designSystematic review
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
Published2018
Admission routes1
Has abstractyes

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