Nonattendance at a Hospital-Based Otolaryngology Clinic: A Preliminary Analysis within a Universal Healthcare System
Bibliographic record
Abstract
Missed appointments at specialty clinics generate concerns for physicians and clinic administrators. Appointment nonattendance obstructs the provision of timely medical interventions and the maximization of systemic efficiencies. Yet, empiric study of factors associated with missed appointments at adult specialty clinics has received little attention in North America. We conducted a preliminary study of otolaryngology clinic nonattendance in the context of a universal healthcare system environment in Canada. Our data were based on the schedule of 1,512 new patient appointments at a hospital-based clinic from May 1 through Sept. 30, 2008. Gathered information included the employment status of the attending physician (i.e., full-time vs. part-time), the patient's sex and age, the day of the week and the time of the appointment, and the attendance status. We found that the rate of nonattendance was 24.4% (n = 369). Nonattendance rates varied significantly according to physician employment status (more common for part-time physicians), patient sex (women) and age (younger adults), and the day of the appointment (Wednesdays), but not according to the time of day. Our findings suggest that there are predictable patient and systemic factors that influence nonattendance at medical appointments. Awareness of these factors can have implications for the delivery of healthcare services within a universal healthcare context.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".