Why Didn't You Call Me? Factors Junior Learners Consider When Deciding Whether to Call Their Supervisor
Bibliographic record
Abstract
OBJECTIVE: Over half of junior learners (JL) feel pressure to work independently and report rarely calling their supervisor. It is unclear how JL decide whether or not to call their supervisor. The study aims to identify factors that JL consider when responding to clinical scenarios and deciding whether to call senior residents (SR) and compare them to factors identified by SR. METHODS: Fifteen cognitive interviews were conducted with SR and JL. Participants were given 8 to 15 sample pages and probed regarding the factors they considered when triaging the page and deciding whether to inform a SR. De-identified interview transcripts were inductively coded using an interpretative phenomenological analysis (IPA) approach. SETTING: Department of Surgery, Faculty of medicine at the University of Ottawa in Canada. PARTICIPANT: Five general surgery SR and ten JL, which included 5 senior medical students and 5 general surgery junior residents. RESULTS: JL and SR indicated a clear need to call SR when managing high acuity pages, which included hemodynamic instability, decreased level of consciousness, or codes (ie, trauma, cardiac arrest). In the absence of high acuity findings, JL judged whether to call SR based on 10 patient and learner-related factors. Patient-related factors include: 1) time since surgery, 2) patient appearance, 3) patient requires intervention, and 4) lack of improvement after initial independent management attempt. Learner-related factors were categorized into clinical (5-8) and social factors (9-10): 5) nurse's level of concern, 6) familiarity with the patient, 7) gut feeling, 8) prior experience managing this presentation, 9) time of day, and 10) interpersonal dynamic with SR. While SR identified all patient-related and clinical factors, they did not cite the 2 social factors JL considered. CONCLUSION: When pages lack high-acuity findings, JL consider various patient and learner factors when deciding whether to inform SR. Discussing these factors may help guide new JL regarding when they should call their supervisor. Understanding social factors is important to create a culture that minimizes their influence on JL's decision-making and promotes patient safety.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".