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Record W110513805

The benefits of implementing a pet insurance program for clinic staff

2002· article· en· W110513805 on OpenAlexaboutno aff
George Guernsey

Bibliographic record

VenueEurope PMC (PubMed Central) · 2002
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryPaymentCeiling (cloud)WageBusinessRevenueMedicineActuarial scienceFamily medicineFinanceEconomicsLabour economics
DOInot available

Abstract

fetched live from OpenAlex

In general economic terms, veterinary income is well down on the salary scale for professionals. As a direct consequence, salaries for nonveterinary employees have been described as “appallingly low,” after results from both the Ontario Veterinary Medical Association and the Nova Scotia Veterinary Medical Association nonveterinarian wage reports were published (1). Further, it has been documented that for a typical client, the ceiling for a veterinary bill when euthanasia becomes a viable option is in the $550 to $600 range, and that the ceiling would rise to $2000 with pet insurance (2). The need for “third party” payment of fees is obvious. Currently, veterinary practitioners are only just beginning to realize the benefits that well-designed pet insurance policies can provide — not only to their clients and patients, but also to themselves. The percentage of pet owners in North America who have pet insurance is very low — less than 1% of pets are insured, while in the United Kingdom and Europe close to 20% are insured (1). This opportunity for growth provides an effective method of assisting the profession out of the financial doldrums. Most veterinary practices provide medical and surgical care for employees' pets as an “in-house” benefit, by significantly discounting the services. Even with the discount, clinic staff members are often placed under a difficult financial burden, while at the same time the practice is financially short-changed. It is probably fair to say that, under these circumstances, the care that could be provided for an employee's pet is often compromised. With pet insurance, the need for care for the employee's pet at emergency or specialty facilities will become a viable option for the employee, and the practice will receive full payment for services rendered. Veterinary technicians, assistants, and receptionists are among the most pet-oriented people, for whom the human/animal bond is very strong. The benefits of them having pet insurance as a staff benefit can only enhance the hospital working relationship. There are significant economic and marketing benefits for practices that participate in an employee pet insurance program. By receiving the “full value” of pet insurance, the employee and the whole health care team will become familiar with the range of services offered and will also become aware of the “intrinsic value” of knowing that their pets will be well cared for. Staff familiarity with the insurer's policies will provide more positive messaging to clients. This should result in reduced confusion about the pet insurance industry, as it currently exists, and an increased number of insured clients' pets in the practice. Practice income will be increased for 2 reasons: less discounting of services and more procedures done for both employees and regular clients. Further, as with any business, certain sound business principles apply to veterinary practices. One of these is to fix costs, wherever possible. As a staff benefit, an employee pet insurance program addresses this perfectly. There is a huge economic opportunity for all veterinary practice owners, not only to embrace the pet insurance concept, but also to enhance that transition through an employee pet insurance program as a staff benefit. George Guernsey

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0280.004

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 head0.227
GPT teacher head0.437
Teacher spread0.210 · 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
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
Published2002
Admission routes1
Has abstractyes

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