‘Meet and greet’ intake appointments in primary care: a new pattern of patient intakes?
Bibliographic record
Abstract
Background: Family physicians (FPs) are expected to take on new patients fairly and equitably and to not discriminate based on medical or social history. 'Meet and greet' appointments are initial meetings between physicians and prospective patients to establish fit between patient needs and provider scope of practice. The public often views these appointments as discriminatory; however, there is no empirical evidence regarding their prevalence or outcomes. Objectives: To determine the proportion of FPs conducting 'meet and greets' and their outcomes. Methods: Study design and setting: Census telephone survey of all FP practices in Nova Scotia (NS). Participants: Person who answers the FP office telephone. Main Outcomes: Proportion of FPs holding 'meet and greets'; proportion of FPs conducting 'meet and greets' who have ever decided not to continue seeing a patient after the meeting. Results: 9.2% of FPs accept new patients unconditionally; 51.1% accept new patients under certain conditions. Of those accepting patients unconditionally or with conditions, 46.9% require a 'meet and greet'; 41.8% have a first-come, first-serve policy. Among FPs who require a 'meet and greet', 44.0% decided, at least once, not to continue seeing a patient after the first meeting. Conclusion: 'Meet and greets' are common among FPs in NS and result in some patients not being accepted into practice. More research is needed to understand the intentions, processes, and outcomes of 'meet and greets'. We recommend that practice scope be made clear to prospective patients before their first visit, which may eliminate the need for 'meet and greets'.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".