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Record W2726270653 · doi:10.1093/geroni/igx004.3717

INNOVATIVE INTERVENTIONS IN ELDER ABUSE PREVENTION AND MITIGATION

2017· article· en· W2726270653 on OpenAlexaffabout
Gloria Gutman, Craig W. Thomas

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychological interventionNeglectAgency (philosophy)Elder abuseGovernment (linguistics)Strengths and weaknessesPolitical sciencePublic relationsPresentation (obstetrics)MedicineNursingPublic administrationPsychologySuicide preventionPoison controlEnvironmental healthSociologySocial psychologySocial science

Abstract

fetched live from OpenAlex

Since the mid-1970s when elder abuse first came to public attention in the most developed countries, many interventions have been developed by government agencies, NGOs, and other concerned bodies to address abuse and neglect of older adults. In this symposium researchers from Ireland, Israel and Canada will highlight innovative approaches to awareness raising, case identification, mitigation and prevention, training and tool development in their respective countries. In the case of Canada, there are two presentations. One focusses on the province of Quebec where there is no specific public agency to counter elder abuse. A project, funded by the Social Sciences Research Council of Canada, aimed at understanding the actions of NGOs and especially volunteer actions to counter elder abuse is described. The second Canadian presentation and those from Ireland and Israel highlight strengths and weaknesses and/or changes over time of selected interventions implemented in health-care and community-based settings and services.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.264
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.036
GPT teacher head0.319
Teacher spread0.284 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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