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Record W4296643262 · doi:10.1111/scs.13121

The ten stories: Intergenerational transfer of values

2022· article· en· W4296643262 on OpenAlexafffund
Mary Ann McColl

Bibliographic record

VenueScandinavian Journal of Caring Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsQueen's University
FundersGovernment of Canada
KeywordsStorytellingNarrativePsychologyNarrative inquiryIdentity (music)TemporalityConstructiveDevelopmental psychologyGender studiesSociologyAestheticsLiteratureArt

Abstract

fetched live from OpenAlex

RATIONALE: A troubling phenomenon for caregivers of elderly parents is their tendency to tell the same stories over and over. Repeated storytelling raises concerns about cognitive decline and memory loss and is often considered a disturbing harbinger of the possible onset of dementia. PURPOSE: This research aims to show that repeatedly told stories are important vehicles for intergenerational transmission of values. METHODS: Using a narrative inquiry approach, this research involved structured interviews with middle-aged adult children, asking them to tell us the stories they felt they were hearing or had heard repeatedly from their aging parent. Interviews were taped and transcribed, then coded for temporality, purpose and content. RESULTS: Based on 126 stories told to 13 participants, it can be confirmed that there are approximately ten stories that older parents repeatedly tell to their adult children, mostly about experiences in their teens and twenties. The majority of the stories are told for the purpose of consolidating the elder's identity or sharing wisdom with the adult child. Key themes in the stories include seeking a better life, youthful fun, upholding standards, sticking together and doing what's right. These themes reflect the significant events and prevailing values of the early to mid-twentieth century. CONCLUSION: This research offers a more constructive way for caregivers to hear the repeated stories told by their aging parents and to offer their loved one the gift of knowing they have been seen and heard.

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)0.000
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 score0.293
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.054
GPT teacher head0.379
Teacher spread0.324 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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