Strategic planning in medical education: enhancing the learning environment for students in clinical settings
Bibliographic record
Abstract
BACKGROUND: The 1999 Cambridge Conference was held in Northern Queensland, Australia, on the theme of clinical teaching and learning. It provided an opportunity for groups of academic medical educators to consider some of the challenges posed by recent changes to health care delivery and medical education across a number of countries. PURPOSE: This paper describes the issues raised by the practical challenges posed by the current environment and how they might be addressed in ways that could promote more effective learning in clinical settings. METHOD: A SWOT analysis is a tool that can help in forward planning by identifying the strengths, weaknesses, opportunities and threats presented by any situation. Our SWOT analysis was used to generate a list of items, from which we chose those most feasible and most likely to promote positive change. RESULTS: Twenty different issues were identified, with four of them chosen by consensus for further elaboration. The discussion gave rise to four main recommended strategies: ensuring that clinical teachers thoroughly understand the purpose and process of learning in clinical settings; equipping learners with 'survival skills'; making the best use of learning resources within different clinical environments and making judicious use of information technology to enhance learning efficiency. CONCLUSIONS: The four strategies were selected not only because of their inherent importance, but also because of their feasibility. Modest changes can motivate students to feel part of a clinical team and a 'community of practice' and enhance their capacity for self-regulated practice.
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 | 0.032 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".