Missed appointments at maternal healthcare clinics in primary healthcare centres in Riyadh city: reasons and associated factors
Bibliographic record
Abstract
Background: The issue of missed appointments at maternal healthcare clinics in primary healthcare centers (PHCCs) has received increasing attention in recent years. The significant relationship between missed appointments and access to maternal healthcare has been recognized around the world. Missed appointments have serious health and economic consequences for women seeking maternal healthcare at PHCCs. Objectives: The objectives of this research were 1) to critically explore the reasons for and socio-demographic factors associated with missed appointments at maternal healthcare clinics at PHCCs; and 2) to provide recommendations for health policy that might help to eliminate the problem of “no-show” maternal patients. Methods: Descriptive statistics were used to analyze the responses of 250 women regarding demographics, as well as their reasons for missing appointments and their preferences regarding appointment confirmation at maternal healthcare clinics at five PHCCs in Riyadh, Saudi Arabia. Results: The most frequent reasons associated with missed appointments reported by women included a lack of supplies and medical equipment, such as ultrasound machines, the unavailability of transportation and a lack of respect from PHCC staff. Conclusion: Developing easily accessible, flexible, interactive appointment systems with reminder/recall, providing means of transportation, providing training courses to PHCC employees on how to address these women and the provision of necessary medical equipment and facilities, such as ultrasound machines to all PHCCs are highly recommended to reduce the occurrence of missed appointments at maternal healthcare clinics at PHCCs in Riyadh, Saudi Arabia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".