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Record W3176954934 · doi:10.26443/ijwpc.v8i2.305

Connecting again with elders in our community: A project to stay together during COVID-19 restrictions and beyond

2021· article· en· W3176954934 on OpenAlexaffvenueabout
Sonia Berkani, Syeda Nayab Bukhari, Soham Rej, Paola Lavín

Bibliographic record

VenueInternational Journal of Whole Person Care · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteJewish General Hospital
Fundersnot available
KeywordsSocial isolationPsychosocialCoronavirus disease 2019 (COVID-19)Context (archaeology)Intervention (counseling)AnxietyIsolation (microbiology)Government (linguistics)MulticulturalismMental healthPsychologyPhonePandemicGerontologySocial supportTelehealthMedicineTelemedicineHealth carePsychiatrySocial psychologyPolitical scienceDisease

Abstract

fetched live from OpenAlex

COVID-19 government regulations resulted in increased social isolation, risk of stress, depression, anxiety, cognitive decline, and re-hospitalization. Telehealth has been highlighted as a potential bridge for healthcare needs, especially in the COVID-19 context. In response to this need our group developed a multicultural, intergenerational, community-based psychosocial intervention. We trained more than 300 volunteers who were able to provide friendly phone support in more than 17 languages to more than 600 older adults across the Greater Montreal Area who could benefit from social connection and support to access community resources. The experience has been heartwarming and facilitating enriching life experiences for seniors, volunteers, and clinicians alike. Furthermore, some preliminary observations suggest that this intervention might have positive effects on the seniors’ mental health.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0190.005
Scholarly communication0.0030.003
Open science0.0020.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.058
GPT teacher head0.396
Teacher spread0.339 · 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 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

Citations0
Published2021
Admission routes3
Has abstractyes

Explore more

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