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A perfect storm?

2013· editorial· en· W2220788790 sur OpenAlexaboutno aff
Jim Fairles

Notice bibliographique

RevuePubMed · 2013
Typeeditorial
Langueen
DomaineHealth Professions
ThématiqueVeterinary Practice and Education Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWonderStatus quoPolitical scienceRevenuePublic relationsHistoryLawPsychologyBusiness
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Though Superstorm Sandy and its sidekick the Nor’easter seem a distant memory in 2013, the weather system devastated parts of the Northeastern United States and Canada. Our thoughts go out to all of the people and animals that were displaced by the disaster. We hope that their situations have improved and life is closer to normal. We salute all those involved in the animal rescues and hope for a quieter new year. As we head into 2013 it is evident that there are many issues facing veterinary medicine in the near and far future. As I travel around the country and listen to veterinarians in various parts of Canada, I wonder if we are at the cusp of a “perfect storm.” Consider, for example, what I believe to be the top 5 issues facing veterinary medicine in Canada today. Number 1: Stagnation of veterinary practice growth. This condition was recognized in the United States much earlier than in Canada — but the issue is certainly being discussed and debated in this country now as well. The term I heard for this last weekend was “the new growth in gross revenue is simply staying at status quo and not falling behind.” There is now (and always has been) more to practice viability than growth with a plethora of tools to aid in overall veterinary practice health. Discussion is ongoing around several objectives to help “turn things around.” These include developing programs to drive more of the pet-owning public to veterinary care, creating wellness programs, and advocating more effective use of social media and the internet with respect to both provision of information and addressing drug supply issues. Number 2: Supply of veterinarians. There has been much written on this subject. If we look back in history most articles pointed to undersupply of veterinarians. This is especially true of the food animal sector. Currently, there is some discussion that undersupply may not be the correct term. The problem may be decreased animal numbers in rural areas creating a lack of economic viability for veterinarians to service these areas. This is an incredibly complex subject; one that will continue to foster further analysis and discussion. With the demand for veterinary education still strong, academic institutions are continuing to increase supply. Have we reached the point of oversupply? Veterinary education is broad, which leads to an incredible number of opportunities beyond traditional practice roles. We must continue to look at the core competencies of graduating veterinarians and how new veterinarians can take advantage of all the opportunities available. Is it time for further differentiation in education delivery? Number 3: New competitive pressures. There are many and varied pressures including “Dr. Google.” New non-traditional methods of veterinary care delivery continue to impact on the way veterinary medicine is “practised.” The commoditization of many aspects of veterinary medicine forces us to look at new and innovative methods of service delivery including those mentioned in my first concern. One of the new “buzz words” is that we must move beyond the “service economy” into the “experience economy” (1) and give clients an experience that they will remember and for which they will pay. In food animal veterinary practice, changes in veterinary care delivery has become a topic of increased discussion. Traditional individual animal medicine is still important but does not fit as well with large herd and flock management. Consulting practice goes so far but still does not tie the veterinarian directly to a specific farm. In some instances we must move to an integrated model wherein our services and fees are integrated with production. Number 4: “Disjointed” veterinary practice. While practising I considered myself a “James Herriot” style veterinarian as I was exposed to and worked in a diverse veterinary medical environment. Specialization is great for veterinary medicine and provides many more opportunities for consultation, treatment and surgery. What we don’t want to leave behind is the “family” or herd veterinarian who is available as the point person and has the broad knowledge of the patient or farm, and the ability to “put everything together.” Veterinarians must continue to promote themselves as point people and as guardians of their clients’ animals’ health. Number 5, and my last concern, is the continued issue surrounding the viability of veterinary self-regulation. Currently in all 10 provinces, the public has put its trust in the regulation of veterinary medicine with our peers. In some instances this can be costly and we sometimes wonder if this is the best way to go. I would maintain that we must continue to guard that which the public has entrusted to us. What better way to be judged than by your peers. A perfect storm? I would suggest we have a perfect opportunity! As a small profession we must continue to ensure we act professionally and continue to strive to better our profession by mitigating all of these concerns. Developing the tools to tackle these issues can only happen with national coordination. What better way to do this than to do this as “one voice and one profession.”

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)0,004
score de la tête « metaresearch » (Gemma)0,020
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,062
Score d'incertitude au seuil0,158

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,020
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0190,010
Communication savante0,0140,015
Science ouverte0,0020,008
Intégrité de la recherche0,0090,021
Charge utile insuffisante (le modèle a refusé de juger)0,0470,013

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 Opus0,229
Tête enseignante GPT0,461
Écart entre enseignants0,232 · 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'étudeSans objet
Domainenon disponible
GenreÉditorial

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
Publié2013
Routes d'admission1
Résumé présentoui

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