Factors associated with patients bypassing primary healthcare centres in Qassim Region, KSA
Bibliographic record
Abstract
This study investigates the reasons for bypassing local primary healthcare centres (PHCs) by patients with minor illnesses in Qassim Region, KSA. A cross-sectional study was performed on 266 patients that visited emergency departments in public hospitals in Qassim Region. The patients were randomly selected and categorised as level five patients (LFPs) using the Canadian Triage and Acuity Scale (CTAS) for patient characterisation. Of the 266 patients, 85.7% had previous experience of visiting PHC facilities. The majority of these patients were not satisfied with their treatment in PHCs. Approximately 52.9% of the patients reported that the working hours at PHCs were not sufficient, 38.1% mentioned a lack of experienced staff, and 31.7% believed that PHCs were insufficient for diagnostic tests. Another 13.8% of the patients reported the unavailability of prescribed medicines. Interestingly, 17.7% of the patients reported that they never bypassed PHCs. In general, the data demonstrate that patients’ gender, employment, and marital status have no significant role in their decision to skip PHCs in favor of emergency departments of public hospitals (p > 0.05). Patients bypassing PHCs without a referral form is a serious concern that have a deleterious effect on the healthcare system, particularly emergency departments. If bypassing continues, it will increase the burden on emergency departments, particularly on healthcare services for the general population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".