Examining non-attendance of doctor's appointments at a community clinic in Saskatoon.
Bibliographic record
Abstract
OBJECTIVE: To quantify the degree of non-attendance of medical appointments, as well as to identify the social reasons behind the missed appointments, at an inner-city primary care clinic. DESIGN: Retrospective chart review and survey. SETTING: Inner-city clinic in Saskatoon, Sask, serving a primarily low-income and First Nations population. PARTICIPANTS: Patients with appointments in the clinic between January 2016 and June 2016. MAIN OUTCOME MEASURES: Number of non-attended clinic appointments and the reasons for missed appointments. RESULTS: Of the 1976 booked appointments during the study period, 487 (24.6%) appointments were not attended. Among the patients with walk-in appointments, 123 (15.5%) of them left the clinic before seeing a physician. New patients had a high rate of non-attendance (44.4% did not show up to appointments). Among those who did not attend an appointment, 19.9% of them missed more than 1 appointment; 77.8% of missed appointments were made more than a week in advance of the appointment, and 51.7% of those who missed an appointment saw a physician at the clinic at a later date (18.5 days later on average). The most common reasons for non-attendance were forgetting the appointment or feeling too sick to attend. Social determinants such as transportation were also found to play a role in non-attendance. Most survey participants stated that a telephone call reminder would aid them in keeping their appointments. CONCLUSION: Non-attendance is a multifactorial issue that causes a considerable waste of resources, limits the provision of preventive care, and negatively affects patient health. As forgetting was found to be a frequent cause of missed appointments, introducing a telephone reminder system might be an affordable and effective first measure to address non-attendance. Factors associated with poverty and other social determinants of health also affect attendance and are more challenging to address.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".