What is Intergenerational Storytelling? Defining the Critical Issues for Aging Research in the Humanities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.178 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.027 | 0.055 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".