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Record W2946689739 · doi:10.18666/trj-2019-v53-i2-9126

Intergenerational Programs: Breaking Down Ageist Barriers and Improving Youth Experiences

2019· article· en· W2946689739 on OpenAlexaff
Sienna Caspar, Erin Davis, Devan Devan Joseph McNeill, Peter Kellett

Bibliographic record

VenueTherapeutic Recreation Journal · 2019
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsAffect (linguistics)RecreationPerceptionExploratory researchGerontologyPsychologyRecreational therapyMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

LINKages, a nonprofit organization, aims to bridge the gap between older and younger generations by building intergenerational programs for youth and older adults. The objective of this project was to explore the influence of the LINKages intergenerational program on: a) older adults’ affect and levels of engagement, and b) youth volunteers’ experiences of engagement and perceptions of older adults. An exploratory case study design was used to address the study objectives. Sixty-five residents from four residential care homes and 87 youth volunteers in the LINKages program participated. Data were collected over 7 months. Statistically significant improvements in students’ perceptions of older adults and their experiences of engagement were found following their participation in the LINKages program. Participation also resulted in positive benefits for residents based on their observed levels of engagement and positive affect.Subscribe to TRJ

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.042
GPT teacher head0.336
Teacher spread0.294 · 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 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

Citations13
Published2019
Admission routes1
Has abstractyes

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