Bibliographic record
Abstract
The number of active clients is, without question, one of the most crucial metrics for any veterinary practice. Without a strong supply of clients, even the best run hospital will falter. While the manipulation of fees, supply costs, and wages can help a practice maximize profitability, the bottom line will always be driven by how many pet owners are walking through the front door. Through the Annual Provincial Practice Owner’s Economic Surveys, the average number of active clients per full-time equivalent DVM can be measured in each of the provinces. For the purposes of this research, a full-time equivalent (FTE) is assumed to be 1750 hours worked per year. Table 1 outlines these averages from 2012 to 2014. The past year was a strong one overall, with most provinces experiencing growth in the average number of active clients per FTE. Led by New Brunswick, with a 20.6% increase, 5 provinces enjoyed higher average numbers of active clients in 2014 compared with those in 2013. Manitoba and Prince Edward Island had jumps of 8.9% and 7.4%, respectively, while Nova Scotia’s average number of active clients per FTE climbed by 4.6%, and Alberta had a modest rise of 2.4%. Table 1 Average number of active clients per FTE DVM (1750 h) On the opposite side of the coin, however, Saskatchewan, British Columbia, and Ontario all witnessed declines in the average number of active clients per FTE. Ontario held the dubious honor of having the steepest downturn, with the number of clients falling by 7.1%. British Columbia’s average slipped by 2.1%, and Saskatchewan’s by 1.2%. Newfoundland & Labrador did not provide sufficient responding practices in 2013 to report on average number of active clients per FTE, and thus is listed as “na” in Table 1. While most provinces will relish these findings, it is important to note that these surveys were being completed just as the price of crude oil began its plummet. As a result, the picture painted by the 2014 survey may be quite different from current reality, particularly in oil-rich provinces such as Alberta, Saskatchewan, and Newfoundland & Labrador. Given these economic developments, some practices will find that they must work even more diligently to get clients into the building. One well-established method for retaining clients is through use of the telephone. By calling, staff members are able to actively engage clients and book an appointment time. Postcards and e-mails are comparatively passive, and place the onus on the client to contact the clinic to schedule an appointment. Another benefit to phoning clients is the ability to respond to any questions or concerns they may have. Without the opportunity to immediately address them, clients’ queries may go unanswered; perhaps they are unsure as to why their pet requires a physical examination. A postcard will do little to answer this question. Although more labor-intensive than mailings, the return on investment of calling clients due for reminders is typically higher as a result of a greater success rate in scheduling appointments. The superiority of the telephone as a method of contacting clients is evidenced through research performed by Terra Shastri of the Ontario Veterinary Medical Association. In her analysis, the number of clients booking an appointment in response to a postcard was compared to the number booking as a result of a phone call (or voicemail if the client did not answer). A postcard or letter resulted in an average of 19% of clients booking an appointment; a phone call garnered an average of 78.7% of clients booking an appointment. This year may prove to be a difficult one for some, particularly if the price of crude oil remains depressed, thus weighing on provincial and national economies. The best defence against these circumstances continues to be a good offence. Getting on the phone and conveying the importance of veterinary visits to clients should be every practice’s game plan. Doing everything possible to keep active client numbers elevated will do more to help a practice weather difficult times than any other measure. Notes: Average numbers of active clients per FTE were determined using the 2014 Provincial Practice Owner’s Economic Surveys. There was no Provincial Practice Owner’s Economic Survey in Quebec in 2014. Number of active clients per FTE was determined by taking each practice’s reported number of active clients, dividing by the total number of DVM hours worked within the practice, and multiplying by 1750. The resulting number of active clients per FTE was averaged among all practices within a province, to determine the provincial average number of active clients per FTE.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".