Transitioning to academia: Exploring the experience of new family medicine faculty members at the beginning of their academic careers.
Bibliographic record
Abstract
OBJECTIVE: To explore the experience of new family medicine faculty members at the beginning of their academic careers and determine what factors might facilitate their transition to an academic role in family medicine. DESIGN: Qualitative, phenomenologic study of new academic family physicians. SETTING: Eight Canadian departments of family medicine. PARTICIPANTS: English-speaking, full-time academic family physicians who had been in their first faculty position in a Canadian department of family medicine for 1 to 5 years. METHODS: Data were collected using semistructured, in-depth interviews that were audiotaped and transcribed verbatim. Data analysis employed an immersion and crystallization technique. The transcriptions were reviewed in an iterative and interpretive manner. Thirteen interviews were performed before saturation was reached. MAIN FINDINGS: The following 3 key themes were identified in relation to the experience of being a new academic family medicine faculty member: lack of or inadequate orientation; the challenges associated with transitioning to academia; and balancing the demands of the role. Orientation was often lacking or suboptimal, with participants left to navigate the transition process alone. The challenges associated with the transition to academia included the realities of clinical work and uncertainties about how to incorporate the various aspects of the new role into members' reality (eg, research). Trying to balance the demands of the academic role (eg, committee involvement, manual reviews), as well as finding work-life balance, was overwhelming. CONCLUSION: This study highlights the factors that might help recruit and retain academic faculty members in family medicine, as well as help them build successful academic careers. Orientation for these new members is an area that requires more attention. Clear parameters around division of time, support, and expectations for advancement should be explained at the beginning of new faculty members' academic appointments. Effective mentoring might help new faculty members have a more successful transition and reduce the risk of feeling overwhelmed and considering leaving academia.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".