Seeking Balance: The Complexity of Choice-Making Among Academic Surgeons
Bibliographic record
Abstract
PURPOSE: This study describes the experiences of academic surgeons in seeking a balance between their personal and professional lives. METHOD: This phenomenological study, conducted in 2009-2010 at the University of Western Ontario, used in-depth individual semistructured interviews to explore the ideas, perceptions, and experiences of 17 recently recruited academic surgeons (nine women/eight men) about seeking balance between their personal and professional lives. All the interviews were audiotaped and transcribed verbatim. The data analysis was both iterative and interpretative. RESULTS: All the participants expressed a passion and commitment to academic surgery, but their stories revealed the complexity of making choices in seeking a balance between their personal and professional lives. This process of making choices was filtered through influential values in their lives, which in turn determined how they set boundaries to protect their personal and family time from the demands of their professional obligations. Intertwined in this process were the trade-offs they had to make in order to seek balance. Some choices, boundary-setting strategies, and trade-offs were dictated by gender. Finally, the process of making choices was not static; instead, the data revealed how it was both dynamic and cyclical, requiring reexamination over the life cycle, as well as their career trajectory. Thus, seeking a balance was an ever-changing process. CONCLUSIONS: Understanding how members of an academic department of surgery navigate the balance between their personal and professional worlds may provide new insights for other disciplines seeking to enhance the development of the next generation of academics.
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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".