Defining Content for a Competency-based (CanMEDS) Postgraduate Curriculum in Ambulatory Care: A Delphi Study
Bibliographic record
Abstract
BACKGROUND: Ambulatory training in internal medicine has been noted to be dysfunctional and inadequate. In this study, we developed a set of competency-based outcomes specific to ambulatory care to guide the design, implementation and evaluation of instructional events to ensure that societal needs are addressed. METHODS: In 2007 a Delphi technique was used to reach consensus and define the priorities for competency-based training in ambulatory care for internal medicine residents. Four groups of stakeholders in Canada participated: program directors, members of the Canadian Society of Internal Medicine, recent graduates, and residents. RESULTS: Two rounds of the Delphi process were required to reach consensus on a set of sixty competency-based educational objectives in ambulatory care that were classified under the CanMEDS roles. The inclusion of recent graduates in this study resulted in the addition of non-clinical topics that would have otherwise been missed, falling under roles historically viewed as being challenging to teach and evaluate (Manager, Health Advocate). CONCLUSION: This study is the first time a Delphi-process has been used to define the priorities for ambulatory care training in internal medicine under a competency-based framework. The resulting compendium of competency-based objectives provides a foundation from which educators can design, evaluate and modify existing training experiences.
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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 | 0.115 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".