Patients' missed appointments in academic family practices in Quebec.
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of no-show patients in 4 family medicine teaching units (FMTUs) and to investigate the reasons given by patients for past missed appointments in order to identify factors that could be acted on to improve access to care. DESIGN: Retrospective data collection through electronic medical records and a self-administered survey. SETTING: Four FMTUs at the University of Montreal in Quebec. PARTICIPANTS: Patients older than 18 years of age (or younger patients' guardians) who were able to read French and had visited the clinic at least once. MAIN OUTCOMES MEASURES: No-show prevalence among patients scheduled to see different types of health care professionals, and patients' reasons for past missed appointments and for not notifying the clinic before missing an appointment. RESULTS: The overall prevalence of no-show patients was 7.8% (2700 missed appointments of 34 619 scheduled appointments), ranging from 6.3% to 9.0% among the 4 FMTUs. The survey participation rate was 91.0% (1757 completed surveys of 1930 distributed surveys). A total of 19.1% of respondents acknowledged previous no-show behaviour. Resolved issues (22.9%) and work obligations (19.4%) were the most frequent personal reasons for missing an appointment, whereas inconvenient timing of the appointment (17.0%), delay before the appointment (14.6%), and lack of confirmation (13.7%) were the most frequent organizational reasons. The most frequent reason for not notifying the clinic of the absence was forgetting to call (55.2%). CONCLUSION: The no-show phenomenon, although not very prevalent in our clinics, is present and can potentially affect access to care. Reasons for missing an appointment without notifying the clinic are varied and point toward different potential solutions to reduce no-shows. Educating patients about the importance of informing the clinic when they cannot come, offering a wider range of appointment dates and times, systematically confirming appointments, improving telephone service, and offering different methods to communicate with the clinic could all be solutions to improve access to care.
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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 | 0.001 | 0.003 |
| 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".