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Record W2097251509 · doi:10.1080/02650530802099817

RECOGNIZING AND RESPONDING TO LOSS AND ‘RUPTURE’ IN OLDER WOMEN'S ACCOUNTS

2008· article· en· W2097251509 on OpenAlexaffabout
Amanda Grenier

Bibliographic record

VenueJournal of Social Work Practice · 2008
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsUnsaidContext (archaeology)Perspective (graphical)NarrativeActive listeningPsychologyHealth careSpace (punctuation)Gender studiesSociologyMedicineSocial psychologyPolitical sciencePsychotherapistHistory

Abstract

fetched live from OpenAlex

In the current context of service, emphasis on the body and impairment mean that emotional experiences are given little space within public health and social care services. Further, as much of what occurs between worker and client remains unsaid, older women's subjective interpretations become more and more difficult to hear in the current context of care. This is especially the case when the age difference of the older women and their workers are considered. In this paper, I focus on how particular types of time‐based statements embedded within older women's narratives on ‘frailty’ can be read around rupture and loss. Drawing attention to these statements highlights the importance of close listening and working therapeutically from the discursive clues of lived experience. Overall, older women's accounts teach us that providing the space to articulate difficult emotions is crucial to fostering connections across age and generational boundaries and allowing older women to articulate their own ‘successful’ responses to the challenges of late life. At the same time, recognizing and accounting for the experiences of loss also forms a strong counter‐perspective to the rational–technical practices increasingly used in Canadian health and social care practices.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.404
Teacher spread0.358 · 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

Citations28
Published2008
Admission routes2
Has abstractyes

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