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Record W3112885485 · doi:10.3390/ani10122412

The Effect of the COVID-19 Pandemic on Riding Lesson Barns and Summer Camps in Ontario

2020· article· en· W3112885485 on OpenAlexaffabout
Katrina Merkies, Caleigh Copelin, Elizabeth Crouchman, Amanda St-Onge

Bibliographic record

VenueAnimals · 2020
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLimitingCoronavirus disease 2019 (COVID-19)BarnHygienePandemicGovernment (linguistics)2019-20 coronavirus outbreakMedicineBusinessMedical educationPsychologyGeographyEngineering

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has direct effects on the operations of riding lesson facilities and summer camps, with little government guidance on how to implement these. An online survey link was distributed to riding lesson facilities in Ontario. Descriptive statistics of respondents (n = 72) reported a decrease in both the number of riding lessons offered and the number of riding students per lesson. Scheduling riding times and limiting access to specific places on the farm ensured controlled access to the farms. Strict hygiene procedures were implemented including disinfecting high contact areas and shared tack. Summer camps followed the same procedures, although some farms chose not to offer camps at all. The use of facemasks was not prevalent in either riding lessons (43.3%) or camps (25%), likely because the activities took place outside. However, recent evidence shows that facemasks are perhaps even more important when outdoors, and it is recommended that riding lesson facilities re-evaluate their requirements for students and staff to wear facemasks while in the barn. In spite of the hardships, many positive aspects were noted including time to attend to repair and maintenance needs, scrutinizing business practices, more respect for barn rules, and more time to bond with the horses.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.322
Teacher spread0.255 · 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 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

Citations5
Published2020
Admission routes2
Has abstractyes

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