Developing a national competency framework for pediatric hospital medicine in Canada using the Delphi method
Bibliographic record
Abstract
Background: Over the past three decades, the field of Pediatric Hospital Medicine (PHM) has expanded rapidly in North America in response to the increasing complexity and acuity of the pediatric inpatient population. While 78 fellowship programs and a published PHM Core Competencies framework exist in the United States, Canadian fellowship programs lack a national competency framework to guide curriculum and practice. This absence creates uncertainty in defining the scope of practice and training expectations for PHM in Canada. The purpose of this study was to define this scope. Methods: Using Delphi methodology, a national panel of experts in PHM iteratively rated potential competencies, on a 5-point scale, to determine their priority for inclusion. Responses were analyzed after each round. Competencies that were assigned a rating of three or less by ≥80% of the panelists were removed from subsequent rounds. The remaining competencies were re-sent to panelists for further ratings until consensus was reached, defined as Cronbach's α ≥0.95 and after a minimum of two survey rounds. At the conclusion of the Delphi process, competencies where ≥80% of the panelists assigned a rating of ≥4 were included. Results: Two rounds of the Delphi process were required to reach consensus. Thirty-five panelists completed both survey rounds. The panelists represented 13 Canadian pediatric tertiary care centers and five community hospitals. Of 176 initial competencies, 109 PHM competencies achieved consensus, spanning the seven CanMEDS roles. Conclusion: This is the first study to define competencies for PHM in Canada. The competencies identified provide a framework for PHM fellowship program directors to shape local curricula. The results may also be used to inform the development of comprehensive national PHM fellowship curricula.
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 | 0.071 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".