How to use agenda-setting: a communication tool to enhance appointment efficiency
Bibliographic record
Abstract
Objective: To describe and demonstrate agenda-setting in clinical practice. Animals: Appointments with any patient(s) can benefit from agenda-setting early in the interaction. Methods: Establishing a mutual agenda is a 9-step process that takes into consideration the client and veterinary professional agendas. Agenda-setting begins with eliciting the client's agenda in 6 steps: eliciting their reasons for the visit, concerns for the pet, and goals and expectations; summarizing back to them; checking for more items; and identifying the client's priority. In the last 3 steps, the veterinary professional shares their agenda items, negotiates a mutual agenda that includes both the client and veterinary professional agendas, and summarizes the shared agenda before a final check for additional items. Results: Agenda-setting is a clinical communication process that supports the development of a shared roadmap for an appointment, leading to enhanced appointment efficiency. Clinical Relevance: Agenda-setting is a practical tool that can be employed by the veterinary team to enhance appointment efficiency, reduce the occurrence of late-rising client concerns, meet client expectations, and enhance client satisfaction.
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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 | 0.052 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".