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Record W4224316771 · doi:10.1007/s10912-022-09735-4

What is Intergenerational Storytelling? Defining the Critical Issues for Aging Research in the Humanities

2022· article· en· W4224316771 on OpenAlexaff
Andrea Charise, Celeste Pang, Kaamil Ali Khalfan

Bibliographic record

VenueJournal of Medical Humanities · 2022
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of WindsorThe Scarborough HospitalUniversity of TorontoToronto Public Health
Fundersnot available
KeywordsPsycINFOStorytellingScopusMEDLINEReflexivityPsychologyNarrativeSociologySocial sciencePolitical science

Abstract

fetched live from OpenAlex

Intergenerational storytelling (IGS) has recently emerged as an arts- and humanities-focused approach to aging research. Despite growing appeal and applications, however, IGS methods, practices, and foundational concepts remain indistinct. In response to such heterogeneity, our objective was to comprehensively describe the state of IGS in aging research and assess the critical (e.g., conceptual, ethical, and social justice) issues raised by its current practice. Six databases (PsycINFO, MEDLINE, PubMed, Scopus, AgeLine, and Sociological Abstracts) were searched using search terms relating to age, intergenerational, story, and storytelling. Peer-reviewed, English-language studies conducted with participants residing in non-clinical settings were included. One thousand one hundred six (1106) studies were initially retrieved; 70 underwent full review, and 26 fulfilled all inclusion criteria. Most studies characterized IGS as a practice involving older adults (> 50 years old) and conventionally-aged postsecondary/college students (17-19 years old). Typical methodologies included oral and, in more recent literature, digital storytelling. Critical issues included inconsistently reported participant data, vast variations in study design and methods, undefined key concepts, including younger vs. older cohorts, generation, storytelling, and whether IGS comprised an intentional research method or a retrospective outcome. While IGS holds promise as an emerging field of arts- and humanities-based aging research, current limitations include a lack of shared data profiles and comparable study designs, limited cross-cultural representation, and insufficiently intersectional analysis of widespread IGS practices. To encourage more robust standards for future study design, data collection, and researcher reflexivity, we propose seven evidence-based recommendations for evolving IGS as a humanities-based approach to research in aging and intergenerational relations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.178
metaresearch head score (Gemma)0.313
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.178
Threshold uncertainty score0.943

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1780.313
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0190.019
Science and technology studies0.0050.033
Scholarly communication0.0270.055
Open science0.0040.010
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.297
GPT teacher head0.523
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations10
Published2022
Admission routes1
Has abstractyes

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