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Learning From the Experiences of Older Adult Volunteers in Sport: A Serious Leisure Perspective

2010· article· en· W218045864 on OpenAlexaff
Katie Misener, Alison Doherty, Shannon Hamm‐Kerwin

Bibliographic record

VenueJournal of Leisure Research · 2010
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsWestern University
Fundersnot available
KeywordsPerspective (graphical)PsychologyContext (archaeology)Interpersonal communicationLeisure activityInterpersonal relationshipRecreationOlder peopleSocial psychologyLeisure timeGerontologyPhysical activityMedicine

Abstract

fetched live from OpenAlex

A sample of older adult volunteers (N = 20, 65 years and older) in community sport organizations was interviewed in order to understand their experiences with volunteering. An interdisciplinary framework of serious leisure, older adult volunteering, and older adult leisure was used to interpret the findings. Volunteering in this context was found to be consistent with serious leisure based on characteristics such as substantial involvement, strong identification with the activity, and the need to persevere. Older adults viewed their experience as extremely positive, enabling them to make a meaningful contribution and to receive several benefits of participation. The most frequently noted negative experience was interpersonal relations, yet overall, this was not enough to drive participants away from this activity. Implications for enhancing older adult volunteering are discussed and avenues for future research are provided.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.388
Teacher spread0.355 · 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 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

Citations92
Published2010
Admission routes1
Has abstractyes

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