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Record W4404553430 · doi:10.33137/utjph.v5i1.44247

What’s Your Aging Story? How Digital Storytelling Can Change the Way We Look at Aging.

2024· article· en· W4404553430 on OpenAlexaboutno aff
Michelle Goonasekera, Shanuki Goonasekera

Bibliographic record

VenueUniversity of Toronto Journal of Public Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsStorytellingCellular AgingAestheticsHistoryArtLiteratureNarrativeChemistry

Abstract

fetched live from OpenAlex

Ageism has far-reaching consequences on the health of older individuals, affecting multiple dimensions of well-being. It not only reduces life expectancy but also has detrimental effects on physical and mental health. Furthermore, ageism amplifies feelings of social isolation and loneliness while restricting older people's access to employment, education, and healthcare services, all of which play a significant role in determining overall health. As the global population ages, addressing ageism becomes increasingly important. Digital storytelling can be a powerful tool to dismantle ageism by amplifying stories of aging, challenging stereotypes, and increasing education and awareness while reaching a large audience. The Age Collective, an online platform, was developed to share diverse stories of aging, encompassing both challenges and triumphs across different age groups in order to raise awareness of and dismantle ageism. We recruited participants through word of mouth in Edmonton, Alberta and the Greater Toronto Area, Ontario. We conducted interviews using audio or video equipment. Interview questions were tailored to the participant to reveal their unique story. Participants were also photographed. These interviews were condensed into short stories or videos with participant input and published on the project’s Instagram page (@theagecollective), YouTube (@TheAgeCollective) and website (theagecollective.com). Educational infographics on aging and ageism were also created and shared on Instagram. From Feb 2022 - Aug 2024, we have shared stories from 33 participants and created 27 educational infographics. We have 930 followers and received 4,272 likes on the Instagram page. The website, established in June 2023, has achieved a unique viewership of 1,562 in the past 30 days. In the digital age, initiatives like digital storytelling can reach global audiences, crossing cultural barriers and sparking international movements. By sharing personal aging stories, digital storytelling can humanize aging, dismantle ageism, and foster connections across generations in order to positively affect the health and well-being of older adults.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
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 head0.130
GPT teacher head0.345
Teacher spread0.216 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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