Identifying the Priority Topics for the Assessment of Competence in Care of the Elderly
Bibliographic record
Abstract
BACKGROUND: With Canada's senior population increasing, there is greater demand for family physicians with enhanced skills in Care of the Elderly (COE). The College of Family Physicians Canada (CFPC) has introduced Certificates of Added Competence (CACs), one being in COE. Our objective is to summarize the process used to determine the Priority Topics for the assessment of competence in COE. METHODS: A modified Delphi technique was used, with online surveys and face-to-face meetings. The Working Group (WG) of six physicians acted as the nominal group, and a larger group of randomly selected practitioners from across Canada acted as the Validation Group (VG). The WG, and then the VG, completed electronic write-in surveys that asked them to identify the Priority Topics. Responses were compiled, coded, and tabulated to identify the topics and to calculate the frequencies of their selection. The WG used face-to-face meetings and iterative discussion to decide on the final topic names. RESULTS: The correlation between the initial Priority Topic list identified by the VG and that identified by the WG is 0.6793. The final list has 18 Priority Topics. CONCLUSION: Defining the required competencies is a first step to establishing national standards in COE.
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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 | 0.037 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".