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Record W4210795002 · doi:10.1136/bmj.o256

Protecting older adults’ mental health in the pandemic

2022· editorial· en· W4210795002 on OpenAlexaff
Yang Hu, Yue Qian

Bibliographic record

VenueBMJ · 2022
Typeeditorial
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPandemicMental healthCoronavirus disease 2019 (COVID-19)Gerontology2019-20 coronavirus outbreakPsychologyMedicinePsychiatryVirologyInfectious disease (medical specialty)DiseaseInternal medicine

Abstract

fetched live from OpenAlex

The recent rapid rise and ongoing high rates of covid-19 cases, driven by the spread of Omicron, are leading to an increase in hospital admissions and deaths among older adults. 1 Due to their heightened vulnerability to covid-19, over the past two years, older people in many countries have been advised to adhere to household-centred containment measures such as household shielding.2 As a result, older people's face-to-face interactions have been substantially reduced during the pandemic, and their in-person contact with people from other households was largely replaced by virtual contact-for example, via telephone calls, texting, Zoom, FaceTime, and social media.3 Interactions with families and friends not living in the same household have long been known to help sustain people's mental health.These interactions are crucial to improving our mental health because they allow for the exchange of vital material, social, and emotional support.

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)0.027
metaresearch head score (Gemma)0.101
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.063
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.101
Meta-epidemiology (narrow)0.0080.003
Meta-epidemiology (broad)0.0100.006
Bibliometrics0.0080.005
Science and technology studies0.0080.006
Scholarly communication0.0160.011
Open science0.0070.004
Research integrity0.0630.046
Insufficient payload (model declined to judge)0.0230.015

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 head0.050
GPT teacher head0.450
Teacher spread0.400 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2022
Admission routes1
Has abstractno

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