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Record W1828092732

Comparing hours worked

2010· article· en· W1828092732 on OpenAlexaboutno aff
Darren Osborne

Bibliographic record

VenuePubMed Central · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
Fundersnot available
KeywordsWorking hoursDemographyWork (physics)MedicineSocioeconomicsStatutory lawWork hoursGeographyDemographic economicsPolitical scienceEconomicsSociologyLabour economicsLawEngineering
DOInot available

Abstract

fetched live from OpenAlex

The average veterinarian in Canada worked an incredibly civilized number of annual hours in 2008. Last year, the median number of annual hours was 1632 h, which can be translated into 7 h/d, 5 d/wk, with 3 wk vacation and 2 wk of statutory holidays. This is a far cry from the 2130 annual hours recorded in 1992 (1). At an astonishing decrease of 31 fewer hours per year over the past 16 years, the average veterinarian in Canada is working 496 h less than was the case a decade ago. Increased wealth in the profession and the successful pursuit of a professional lifestyle have allowed veterinarians to earn more, while working less. The information provided in this article comes from the results of the 2008 CVMA Practice Owner’s Economic Survey. The estimates are based on 1768 observations and are accurate to +/− 1.8%, 19 times out of 20. While it is clear that the annual number of hours worked has gone down, there are significant variations around the average when the data are broken down by province, type of employment, and ownership status. Generally speaking, practice owners work longer hours than associates, mixed and large animal veterinarians work longer hours than companion animal veterinarians, and there are considerable variations in hours worked from province to province.

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 score0.150
Threshold uncertainty score0.237

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 head0.018
GPT teacher head0.186
Teacher spread0.168 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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