Profile of registrars and reasons for specialization : original research
Bibliographic record
Abstract
Introduction The shortage of general practitioners is a worldwide phenomenon and occurs in countries such as Canada, the United States of America and Saudi Arabia. Increasingly fewer students are interested in general practice as an occupation. Choosing a speciality is a complex process and is dependent on a wide range of intrinsic and extrinsic factors, including preference at the start of studies, experience during undergraduate training, and environmental factors such as cultural and socio-economic background. The aim of this study was to determine the profile of registrars at the Faculty of Health Sciences (University of the Free State), as well as their reasons for specialisation. Method All departments were contacted and the first author circulated questionnaires and informed consent forms during the academic afternoons. Questionnaires and informed consent forms were available in Afrikaans and English. Results Of the 150 questionnaires handed out, 109 were used for analysis (122 were received, of which 13 were incomplete). Most of the respondents were Afrikaans speaking (81.7%), male (68.8%), with a median age of 31 years (range 26 to 50 years), and married (67.6%). Only 13.8% of the respondents' fathers were medical doctors, 54.5% of which were general practitioners. Few respondents (13.8%) had needed to repeat any of their undergraduate studies, while 23.9% had received academic prizes. Most (91.7%) had completed family medicine during rotation in undergraduate training. The main specialisation areas were internal medicine, obstetrics and gynaecology, and radiology. Many respondents (76.9%) had worked in general practice or primary care for a median of two years (range 0.25 to 18 years). The main reasons for leaving primary care were overwork, wanting to function at a higher level, and that they saw no pathologies. Half of the respondents (49.9%) had never considered private practice as an occupation and the main reasons given were type of work and patient, no future for general practitioners, legislation, and inconvenient hours Conclusion This study provides some indicators as to why fewer doctors are willing to work as general practitioners or primary healthcare physicians. An excessive workload in the primary healthcare setting was indicated by 31.3% of the respondents as the major reason why they chose not to stay.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".