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Record W2081123582 · doi:10.1353/ces.2013.0004

“Where We Have Come and are Now Trapped”: Views of Victims and Service Providers on Abuse of Older Adults in Tamil and Punjabi Families

2013· article· en· W2081123582 on OpenAlexvenueaboutno aff
Vappu Tyyskä, Farishta Murzban Dinshaw, Christopher Redmond, Frederica Gomes

Bibliographic record

VenueCanadian ethnic studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsTamilService providerService (business)CriminologySociologyBusinessArtMarketingLiterature

Abstract

fetched live from OpenAlex

This paper contributes to the literature on the intersections of age, generation, immigrant status, and racialization. In particular, it will bring into the discussion: (1) the perspectives of victims and service providers on the causes of, barriers to, and alleviation of abuse of older adults; and (2) continuities and changes in the social meanings of age, including the intersectionalities between age and other social hierarchies, such as immigrant status and membership in a racialized group. Using data obtained from a study consisting of interviews of service providers, and older adult abuse victims in the Toronto Punjabi and Tamil communities, this paper seeks to answer questions regarding the causes of family violence, barriers to obtaining services, and proposed solutions. The main themes arising from the respondents’ views fall within structural and cultural categories, with language barriers, the role of culture, and stigmatization among others emerging as key factors. Importantly, the data suggests that there exists a disconnect between victims and service providers, with the majority of victims supporting an anti-racist approach, while the majority of service providers fall within the multicultural model. This study suggests that the problem of abuse of older adults is compounded by deficient service provision and government policy. The recommended solution is to reach beyond culturally and linguistically specific services toward full engagement with immigrant communities, to prevent further harm toward older immigrant adults. Cet article contribue à la recherche menée aux points de croisement de l’âge, de la génération, du statut d’immigré et de la racialisation. Il enrichit particulièrement le débat portant sur : 1) les perspectives des victimes et des fournisseurs de services sur ce qui cause, entrave ou réduit les abus envers les personnes âgées, et 2) la continuité et les changements au sens social de la vieillesse, y inclus les intersectionalités entre l’âge et d’autres hiérarchies sociales, telles que le statut d’immigré ou l’appartenance à un groupe racialisé. Nous nous fondons sur les données recueillies dans une étude menée à partir d’entrevues avec des prestataires de services et des aînés victimes d’abus dans les communautés punjabies et tamiles de Toronto, pour tenter ici de répondre aux questions sur les causes de la violence familiale, les obstacles à l’obtention de services et les solutions proposées. Les principaux thèmes soulevés du point de vue des répondants tombent dans des catégories structurelles et culturelles, dont les facteurs clés qui émergent parmi d’autres sont la barrière de la langue, le rôle de la culture et la stigmatisation. Il est important de souligner que les données indiquent un fossé entre les victimes et les pourvoyeurs de services, la majorité des unes supportant une approche antiraciste, alors que les autres s’inscrivent en majorité dans le modèle multiculturel. Cette étude donne à penser que le problème de l’abus de personnes âgées est aggravé par une offre de services et une politique gouvernementale déficientes. La solution recommandée est d’aller au-delà de services spécifiquement culturels et linguistiques pour s’engager totalement auprès des communautés immigrées afin d’éviter que les torts envers leurs aînés continuent.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.064
GPT teacher head0.327
Teacher spread0.264 · 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 designQualitative
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

Citations9
Published2013
Admission routes2
Has abstractyes

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