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Record W2220788790

A perfect storm?

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

Bibliographic record

VenuePubMed · 2013
Typeeditorial
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWonderStatus quoPolitical scienceRevenuePublic relationsHistoryLawPsychologyBusiness
DOInot available

Abstract

fetched live from 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.”

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)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0190.010
Scholarly communication0.0140.015
Open science0.0020.008
Research integrity0.0090.021
Insufficient payload (model declined to judge)0.0470.013

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 head0.229
GPT teacher head0.461
Teacher spread0.232 · 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 designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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