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Enregistrement W243172976

2011 report on veterinarians in industry, government, and academe.

2012· article· en· W243172976 sur OpenAlexaboutno aff
Darren Osborne

Notice bibliographique

RevuePubMed · 2012
Typearticle
Langueen
DomaineHealth Professions
ThématiqueVeterinary Practice and Education Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPrivate practiceGovernment (linguistics)MedicineIncentiveCashWageEarningsFamily medicineBusinessPolitical scienceFinanceEconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Canadian veterinarians employed in government, industry, and academe (GIA) saw a drop in their incomes from 2010 to 2011. In a survey of 171 licensed GIA veterinarians, reported net incomes fell 4% from $110 000 in 2010 to $106 000 in 2011. Declining client numbers over the past 2 years have been picking away at incomes for veterinarians in private practice, and the latest results from veterinarians in government, industry, and academe suggest that veterinarians working outside of private clinical practice are not immune to the general economic malaise. The 2011 Survey of Veterinarians in Government, Industry, and Academe is a joint project between the Ontario Veterinary Medical Association, the Canadian Veterinary Medical Association, and the Canadian Animal Health Institute. Veterinarians across Canada are surveyed annually to gather demographic, and wage and benefit data. Income figures are represented in response to the question, “what was your total cash compensation (before taxes/excluding benefits) from your GIA employer (please include all monetary bonuses and sales incentives)?” Because the survey was anonymous, median figures were used to prevent erroneous results from severe outliers. The results are generally accurate to +/− 4.0%, 19 times out of 20. Most veterinarians employed in government, industry, and academe started out in private practice. When they began their training, 82% of current GIA veterinarians reported that they had intended on a career in private practice and 93% indicated they had been employed in private practice at some point in their career. Currently, 14% still work part time in clinical practice. The remaining veterinarians who left private practice for a career in government, industry or academe reported that they left private practice because they wanted to, not because they had to. The predominant reason for the move from private practice to government, industry, or academe was to pursue academic or personal interests. Fewer veterinarians moved away from private practice for negative reasons; some reported a move to reduce hours worked, to reduce stress, for financial reasons, or for health reasons (Table 1). Twenty percent reported that they were disillusioned with private clinical practice. Table 1 Income by type of employment By comparison, veterinarians in private practice earned more than veterinarians in government, industry, and academe. Even when broken down into the type of employment, veterinarians in private clinical practice earn more than the highest paid GIA veterinarians. For 2010, the highest paid veterinarians outside of private practice were those employed by industry or pharmaceutical companies. The second highest held positions were in universities and colleges. The ranks flipped in 2011, however, when industry/pharmaceutical veterinarians experienced a 5.6% drop in salaries while their counterparts in university and college experienced a 2% gain. Veterinarians employed at colleges and universities earned median salaries of $124 500 and those employed by industry and pharmaceutical companies dropped to $118 000. (The gains and losses in Table 1 come from the same sample. Respondents to the 2011 survey were asked to report personal incomes from 2010 and expected incomes for 2011.) Government-employed veterinarians earned the lowest incomes for both 2010 and 2011. Federal government employed veterinarians who earned $101 000 in 2010 saw a slight gain of 0.4% for 2011. Veterinarians in provincial government earned just under their federal counterparts with a median income of $100 000 in 2010 and after experiencing a drop of 1.5% saw their incomes fall to $98 500 for 2011 (Table 1). Seniority accounted for higher incomes for veterinarians in industry, government, and academe but new hires seemed to buck the trend. Veterinarians with less than one year experience in their current position earned median annual incomes higher than veterinarians with 6 to 10 years experience in their present position. Veterinarians with 1 to 2 years experience had the lowest annual incomes at $97 500. Those with 3 to 5 years experience earned 2% higher incomes at $99 500 and incomes jumped 6.5% for veterinarians with 6 to 10 years experience. GIA veterinarians with the most seniority (more than 10 years experience) earned median salaries of $130 000, 22.6% higher than the average. For the past 2 years, veterinarians in private practice have seen a decrease in active clients and have struggled to maintain their incomes. Veterinarians outside of clinical private practice employed in government, industry, and academe have suffered the same fate in 2011. Annual incomes were down 4% in the past year with the highest drop coming from the top category earner (industry/pharmaceutical), where incomes fell 5.6%. There was good news for newly hired veterinarians, who saw higher than average starting salaries suggesting a new trend in compensation, which could pull GIA veterinary incomes closer to their private practice counterparts.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,869
Score d'incertitude au seuil0,263

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0060,011
Études des sciences et des technologies0,0020,000
Communication savante0,0020,001
Science ouverte0,0020,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0420,017

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,412
Tête enseignante GPT0,474
Écart entre enseignants0,062 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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