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Record W2727147309 · doi:10.1093/geroni/igx004.445

ELDER ABUSE IN CANADA: A GROWING DILEMMA IN AN AGING SOCIETY

2017· article· en· W2727147309 on OpenAlex
Elizabeth Podnieks

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeglectElder abuseDilemmaPresentation (obstetrics)Depression (economics)PopulationGerontologyPopulation ageingPsychologyPsychological abuseChild abuseMedicinePsychiatrySuicide preventionPoison controlMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

According to Statistics Canada, eight million adults will be over the age of 65 by 2031, nearly 25 percent of the population. Increasingly, older adults report being victims of abuse, even though Canada has actively addressed the problem since the early 1980s (Podnieks, 1989). This presentation describes the most recent study to quantify the extent of elder abuse and neglect in Canada (McDonald, 2016). More than three quarters of a million Canadian elders suffered some form of abuse last year, more than double the 1998 finding. One reason could be a rise in financial abuse, the second most frequent form behind psychological abuse. The most important risk factor was depression, followed by having been abused in another stage of the life course. This presentation describes the study’s guiding theoretical framework, methodology, and findings and draws conclusions and offers implications for future research and services for maltreated older adults in Canada.

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.

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)0.000
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 score0.042
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.332
Teacher spread0.290 · 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