What are the factors influencing Canadian-trained residents’ choice of pursuing the subspecialty of Maternal-Fetal Medicine?
Bibliographic record
Abstract
An increasing need for Maternal-Fetal Medicine (MFM) physicians in Canada has been reported, along with decreasing resident interest in the subspecialty. This study was designed to explore the factors influencing Canadian-trained residents’ career choice of Maternal-Fetal Medicine (MFM), focusing on their perceptions of MFM residency and career, the positive and negative influencing factors, and how MFM could be perceived as a more attractive career choice by residents. Twenty-one residents from Canadian Obstetrics and Gynecology (O&G) and MFM residency programs participated in semi-structured telephone interviews. A qualitative approach was selected, and interview data were analyzed using a thematic analysis approach, drawing on constructivist grounded theory techniques. Seven themes influencing resident perception of MFM were identified, including the field of MFM, O&G residency experiences, the MFM residency program, perceived variety of MFM practice, lifestyle of MFM, academic career, and finances. Current trainees identified the field itself, a dislike of gynecology, academic practice, and mentorship from MFM faculty as positive factors influencing their choice of MFM. Residents viewed the emotional toll of MFM practice, increasing demand and burnout, patient complexity, the exclusion of gynecology, and their O&G residency MFM experience as negative factors pushing them away from MFM. The resident perception of positive and negative influencing factors varied by their general favourability towards MFM. Factors intrinsic and extrinsic to MFM were identified, as well as potential changes to attract residents to the subspecialty, including opportunities for change within O&G residency, MFM residency, and gynecology practice as part of a MFM career. This study revealed several novel and contemporaneous factors influencing MFM subspecialisation decision-making, including the field of MFM itself, exposure to MFM residents and residency program requirements, and the impact of staff physician burnout on residency education and career choice. The results have implications for O&G and MFM postgraduate education, as well as for the subspecialty of MFM in Canada. Further research is needed to (1) define Canadian MFM practice, (2) determine accurate workforce needs, (3) assess the effect of physician burnout on trainees, and (4) resolve the question of gynecology practice in MFM.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".