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Record W1754578044 · doi:10.5334/ijic.1082

Key focal areas for bridging the fields of aging and disability: findings from the growing older with a disability conference

2012· article· en· W1754578044 on OpenAlexafffundabout
Vishaya Naidoo, Michelle Putnam, Andria Spindel

Bibliographic record

VenueInternational Journal of Integrated Care · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsMarch of Dimes CanadaYork University
FundersGovernment of CanadaMarch of Dimes CanadaMarch of Dimes Foundation
KeywordsBridging (networking)DeclarationGerontologyPsychologyMedical model of disabilityHealthy agingPublic relationsPolitical scienceMedicineComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Based upon research presented at the 2011 Festival of International Conferences on Caregiving, Disability, Aging and Technology (FICCDAT)-and specifically the Growing Older with a Disability (GOWD) conference, this paper identifies areas where bridging building between aging and disability is needed to support older adults aging into or with disabilities. Five focal areas emerged: 1) The Need to Forward Bridging Between Aging and Disability Sectors, 2) Theoretical Frameworks of Individual Aging that Facilitate Bridging, 3) Bridging through Consumer Participation and Involvement, 4) Bridging Through Knowledge Transfer and 5) Bridging Opportunities in Long-Term Supports and Services and Assistive Technologies. Discussion of themes is provided within both international and Canadian contexts, reflecting the interests of FICCDAT and GOWD organizers in discussing how to improve bridging in Canada. Findings from this report form the basis of the Toronto Declaration on Bridging Aging and Disability Policy, Practice, and Research.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.297
Teacher spread0.278 · 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.

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

Citations12
Published2012
Admission routes3
Has abstractyes

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