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Record W4402991870 · doi:10.1080/14927713.2024.2410185

Unlocking the secrets of change: ethnographic insights into older adults’ leisure time physical activity during COVID-19

2024· article· en· W4402991870 on OpenAlexvenueno aff
Rasool Norouzi Seyed Hossini

Bibliographic record

VenueLeisure/Loisir · 2024
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakLeisure timeEthnographySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Physical activityGerontologyPsychologyMedicineSociologyVirologyPhysical medicine and rehabilitationAnthropologyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic brought about significant alterations in various aspects of society, yet its impact on the leisure time physical activity (LTPA) of older adults still needs to be explored. Existing research points to substantial transformations in recreational and sports environments due to the pandemic. This study aims to bridge this gap by conducting ethnographic fieldwork spanning 17 months, delving into how Iranian older adults adapted their LTPA during this crisis. Data were gathered through immersive field experiences, detailed field notes, and opportunistic interviews. The findings reveal two primary shifts in the LTPA of older adults: (1) Disruption of social interactions within physical activities and (2) Psychological uncertainty and perplexity. In response, older adults have employed two coping mechanisms to navigate these changes. This research underscores the importance of policymakers reevaluating their approaches and devising strategies to support the continuity of LTPA, especially in exceptional circumstances like the COVID-19 pandemic.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.058
GPT teacher head0.346
Teacher spread0.288 · 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 designBench or experimental
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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