“Everything new is happening all at once”: a qualitative study of early career obstetrician and gynaecologists’ preparedness for independent practice
Bibliographic record
Abstract
Background: The transition from residency training into practice is associated with increasing risks of litigation, burnout, and stress. Yet, we know very little about how best to prepare graduates for the full scope of independent practice, beyond ensuring clinical competence. Thus, we explored the transition to independent practice (TTP) experiences of recent Obstetrics and Gynaecology graduates to understand potential gaps in their perceived readiness for practice. Methods: Using constructivist grounded theory, we conducted semi-structured interviews with 20 Obstetricians/Gynaecologists who graduated from nine Canadian residency programs within the last five years. Iterative data collection and analysis led to the development of key themes. Results: Five key themes encompassed different practice gaps experienced by participants throughout their transition. These practice gaps fit into five competency domains: providing clinical care, such as managing unfamiliar low-risk ambulatory presentations; navigating logistics, such as triaging referrals; managing administration, such as hiring or firing support staff; reclaiming personhood, such as boundary-setting between work and home; and bearing ultimate responsibility, such as navigating patient complaints. Mitigating factors were found to widen or narrow the extent to which new graduates experienced a practice gap. There was a shared sense among participants that some practice gaps were impossible to resolve during training. Conclusions: Existing practice gaps are multi-dimensional and perhaps not realistically addressed during residency. Instead, TTP mentorship and training opportunities must extend beyond residency to ensure that new graduates are equipped for the full breadth of independent practice.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".