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Record W3175082804 · doi:10.1139/apnm-2021-0204

Exploring the impact of COVID-19 on the willingness of older adults to participate in physiology research: views from past and potential volunteers

2021· article· en· W3175082804 on OpenAlexvenueno aff
Colleen S. Deane, Amanda Gates, Gemma Traviss‐Turner, Daniel J. Wilkinson, Kenneth E. Smith, Philip J. Atherton, Bethan E. Phillips

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersNIHR Nottingham Biomedical Research CentreBiotechnology and Biological Sciences Research CouncilVersus ArthritisMedical Research CouncilNational Institute for Health and Care Research
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicineGerontologyVirologyPathologyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

We explored the views of older (≥65 years) past and potential volunteers in regard to participating in physiology research during the COVID-19 pandemic. Using an online questionnaire and focus groups, we found that past volunteers (n = 55) were more likely to take part in both acute (p < 0.05) and chronic (p < 0.05) physiology studies, compared with potential future volunteers (n = 57). Both cohorts demonstrated a positive attitude towards volunteering during the COVID-19 pandemic, although concern was evident. Novelty: Volunteers demonstrated a positive attitude and also concern towards participating in physiology research during COVID-19.

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)0.012
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.222
GPT teacher head0.435
Teacher spread0.214 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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