Assessing leadership in junior resident physicians: using a new multisource feedback tool to measure Learning by Evaluation from All-inclusive 360 Degree Engagement of Residents (LEADER)
Bibliographic record
Abstract
Background The multifaceted nature of leadership as a construct has implications for measuring leadership as a competency in junior residents in healthcare settings. In Canada, the Royal College of Physicians and Surgeons of Canada’s CanMEDS physician competency framework includes theLeaderrole calling for resident physicians to demonstrate collaborative leadership and management within the healthcare system. The purpose of this study was to explore the construct of leadership in junior resident physicians using a new multisource feedback tool. Methods To develop and test the Learning by Evaluation from All-Inclusive 360 Degree Engagement of Residents (LEADER) Questionnaire, we used both qualitative and quantitative research methods in a multiphase study. Multiple assessors including peer residents, attending physicians, nurses, patients/family members and allied healthcare providers as well as residents’ own self-assessments were gathered in healthcare settings across three residency programmes: internal medicine, general surgery and paediatrics. Data from the LEADER were analysed then triangulated using a convergent-parallel mixed-methods study design. Results There were 230 assessments completed for 27 residents. Based on key concepts of theLeaderrole, two subscales emerged: (1)Personal leadership skillssubscale (Cronbach’s alpha=0.81) and (2)Physicians as active participant-architects within the healthcare system(abbreviated toactive participant-architectssubscale, Cronbach’s alpha=0.78). There were seven main themes elicited from the qualitative data which were analogous to the five remaining intrinsic CanMEDS roles. The remaining two themes were related to (1) personal attributes unique to the junior resident and (2) skills related to management and administration. Conclusions For healthcare organisations that aspire to be proactive rather than reactive, we make three recommendations to develop leadership competence in junior physicians: (1) teach and assess leadership early in training, (2) empower patients to lead and transform training and care by evaluating doctors, (3) activate frontline care providers to be leaders by embracing patient and team feedback.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".