Career Mentoring Surgical Trainees in a Competitive Marketplace
Bibliographic record
Abstract
Resident trainees in Canadian Otolaryngology–Head & Neck Surgery (OHNS) programs have cited job prospects as the biggest stressor they face. Increased numbers of residency training positions combined with decreased employment opportunities have worsened competition for surgical positions. The purpose of this inquiry was to explore gaps in resident career planning and examine how leadership can prepare graduating residents to optimize employability. This mixed-methods prospective study was completed in two phases. A combination of online surveys and two focus group sessions were used to gather information from academic and clinical staff surgeons, resident trainees, and administrative leadership. Eleven of the potential 12 resident participants responded to the initial survey, seven of the 13 staff surgeons, and one administrative leader. Each of the resident and staff focus groups had five participants. This comprehensive inquiry led to the development of a conceptual framework describing domains of concern important to OHNS residents. Themes included lack of career mentoring, complex systemic limitations, inadequacy of exposure to community-based surgical practice, and a potentially stifling organizational culture. OHNS residents face significant stress regarding potential employability following residency. Solutions to address concerns must be collaborative in nature and begin with the existing leadership structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".