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Record W3022487216 · doi:10.1097/nhh.0000000000000865

Inclusion and Quality of Life for Older Adults

2020· article· en· W3022487216 on OpenAlexaffabout
Sheryl Reimer‐Kirkham, Ashley DaCosta, Melissa De Boer, Andrea Dresselhuis, Barbara Hall, Paula Optland, Melody Pan, Gwendolyn Williams

Bibliographic record

VenueHome Healthcare Now · 2020
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsBrampton Civic HospitalTrinity Western UniversityAlberta Health ServicesRichmond HospitalWestern UniversityAlberta HealthFoothills Medical CentreProvidence Health CareInterior Health
Fundersnot available
KeywordsInclusion (mineral)Quality of life (healthcare)GerontologyUpstream (networking)Quality (philosophy)PsychologyHealth policySocial policyNursingMedicinePolitical sciencePublic healthSocial psychology

Abstract

fetched live from OpenAlex

Social inclusion and social determinants of health (SDH) are key to healthy aging, and a failure to adequately address these influences can lead to negative health consequences such as the development and worsening of chronic conditions. Health policy is needed that prioritizes aging well in place as an "upstream" approach to address SDH and thereby improve health outcomes and promote quality of life. Globally, nurses are well positioned to advocate for such policy, given their commitment to fostering social inclusion and quality of life for older adults. This article presents a policy submission, made by nurses enrolled in a graduate health policy course, to Canada's Standing Committee on Human Resources and Social Development and the Status of Persons with Disabilities, for the Committee's report on Advancing Inclusion of and Quality of Life for Seniors ().

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)0.002
metaresearch head score (Gemma)0.010
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.087
GPT teacher head0.388
Teacher spread0.301 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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