The extent of interruptions to primary care medical officers’ consultations in the Western Cape
Bibliographic record
Abstract
BACKGROUND: Administrative tasks are an increasing burden for primary care doctors globally and linked to burnout. Many tasks occur during consultations. They cause interruptions with possible effects on patients' and doctors' experiences and care. The burden and typology of interruptions of doctors in primary care consultations have not been studied in South Africa. Given the link between administrative loads and burnout, describing the extent of these interruptions would help. This study's aim was to assess the extent of interruptions on primary care doctors in the Western Cape. METHODS: This was a descriptive cross-sectional survey. Doctors from rural and urban primary care clinics in the Western Cape answered an online self-administered survey on the types of interruptions experienced during consultations. Interruptions were categorised and their prevalence calculated. Clinical and non-clinical interruption categories were compared. RESULTS: There were 201 consultations from 30 doctors. Most interruptions were from retrieving and recording the current patient's information (93.0%), paperwork for other patients (50.7%), and telephone calls about the current patient (41.8%). Other prevalent interruptions were for emergencies (39.8%) and acquiring consumables (37.3%). The median (interquartile range [IQR]) of four (2-4) interruption types per consultation was higher than global settings. CONCLUSION: Doctors experienced many interruptions during consultations. Their wide range included interruptions unrelated to the current patient.Contribution: This study adds insights from the global south on clinicians' administrative burden. It elaborates on the types of activities that interrupt consultations in an upper-middle income primary care setting. Exploration of interventions to decrease this burden is suggested.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".