Fostering excellence: roles, responsibilities, and expectations of new family physician clinician investigators.
Bibliographic record
Abstract
PROBLEM ADDRESSED: A key priority in primary health care research is determining how to ensure the advancement of new family physician clinician investigators (FP-CIs). However, there is little consensus on what expectations should be implemented for new investigators to ensure the successful and timely acquisition of independent salary support. OBJECTIVE OF PROGRAM: Support new FP-CIs to maximize early career research success. PROGRAM DESCRIPTION: This program description aims to summarize the administrative and financial support provided by the C.T. Lamont Primary Health Care Research Centre in Ottawa, Ont, to early career FP-CIs; delineate career expectations; and describe the results in terms of research productivity on the part of new FP-CIs. CONCLUSION: Family physician CI's achieved a high level of research productivity during their first 5 years, but most did not secure external salary support. It might be unrealistic to expect new FP-CIs to be self-financing by the end of 5 years. This is a career-development program, and supporting new career FP-CIs requires a long-term investment. This understanding is critical to fostering and strengthening sustainable primary care research programs.
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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 | 0.050 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".