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Record W3128085445 · doi:10.36502/2020/hcr.6181

From the Mouths of the Elderly: What can their Life Experience Teach us?

2020· article· en· W3128085445 on OpenAlexaffabout
Ami Rokach, Dene S. Berman

Bibliographic record

VenueJournal of Health Care and Research · 2020
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsYork University
Fundersnot available
KeywordsReminiscenceGroup cohesivenessNarrativePsychologyLife reviewGerontologyMedicineSocial psychologyAlternative medicineCognitive psychology

Abstract

fetched live from OpenAlex

Reminiscing by older adults can facilitate beneficial outcomes through the preparation for the end of life, the cohesiveness of life narratives, and the creation of life meanings. Given this, and the historical challenges of communication between generations, the objective of this study was two-fold: (1) to harness the beneficial role reminiscence can play in the mental health of older adults; (2) to facilitate generational learning by documenting and thematically analyzing the experiences and knowledge of older adults. We hypothesized that our interviews, which had the stated goal of helping younger people navigate life challenges, would not only act as a catalyst for the participants to reminisce but also create a corpus of knowledge which could be later transcribed and analyzed into accessible “pearls of wisdom”. The interviews were conducted in Canada with 132 participants who were 60 to 94 years of age with six questions constructed to promote further commentary. Through the interviews, we were successful in producing a large representation of the older adults’ experiences and what they believed would be beneficial for the younger generation. Due to the potential benefits for participants and larger communities, we recommend this approach be adopted for future studies.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.670

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.164
GPT teacher head0.483
Teacher spread0.319 · 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 designQualitative
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
Published2020
Admission routes2
Has abstractyes

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