MétaCan
Menu
Back to cohort
Record W4399140143 · doi:10.3390/healthcare12111111

Can ‘What Is Known’ about Social Isolation and Loneliness Interventions Sufficiently Inform the Clinical Practice of Health Care and Social Service Professionals Who Work with Older Adults? Exploring Knowledge-to-Practice Gaps

2024· article· en· W4399140143 on OpenAlexaff
Salinda Horgan, Jeanette Prorok, David Conn, Claire Checkland, John Saunders, Bette E. Watson-Borg, Lisa Tinley

Bibliographic record

VenueHealthcare · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsCanadian Mental Health AssociationBaycrest HospitalQueen's University
Fundersnot available
KeywordsLonelinessPsychological interventionSocial workIsolation (microbiology)Social isolationSocial careNursingWork (physics)Health carePsychologyHealth professionalsMedicineApplied psychologySocial psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Establishing intervention effectiveness is an important component of a broader knowledge translation (KT) process. However, mobilizing the implementation of these interventions into practice is perhaps the most important aspect of the KT cycle. The purpose of the current study was to conduct an umbrella review to (a) identify promising interventions for SI&L in older adults, (b) interpret (translate) the findings to inform clinical knowledge and practice interventions in different settings and contexts, and (c) highlight research gaps that may hinder the uptake of these interventions in practice. The broader purpose of this study was to inform evidence-based clinical practice guidelines on SI&L for HCSSPs. In line with other reviews, our study noted variations in methods and intervention designs that prohibit definitive statements about intervention effectiveness. Perhaps, the most significant contribution of the current review was in identifying knowledge-to-practice gaps that inhibit the implementation of interventions into practice-based realities.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.738
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.114
GPT teacher head0.487
Teacher spread0.373 · 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.

Study designQualitative
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

Citations6
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueHealthcareSame topicHealth disparities and outcomesFrench-language works237,207