Bibliographic record
Abstract
A symposium on Learning in Primary Care was held in Cape Town, South Africa, as a pre-conference workshop to the 9th International Ottawa Conference on Medical Education. The aim of this report is to inform medical educationalists of important issues in learning in primary care and to stimulate further debate. Four international speakers gave presentations on their experiences in teaching and learning in primary care. Objective positive outcome measures include acquiring clinical skills equally well in general practice as in hospital, and improved history taking, physical examination and communication skills learning. Students regard the course as an essential requirement for learning and are appreciative of the wider aspect to learning provided by the community, giving a more holistic view of health. A SWOT analysis (strengths, weaknesses, opportunities and threats) of teaching and learning in primary care identified that learning in primary care is of a generalist nature and reality based, but is hampered by a lack of resources. The increased professionalization of teaching in primary care results in better training, cost containment, and improved quality of health care at community level. It is important to focus on turning threats into opportunities. Academic credibility needs to be established by conducting research on learning in primary care and developing the conceptual basis of primary care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".