The Loneliness of the Family Medicine Chair: A CERA Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Leadership roles may increase symptoms of loneliness that negatively impact health outcomes and job performance. This study examined loneliness among family medicine department chairs and explored mitigating factors. METHODS: We conducted a cross-sectional survey of family medicine chairs in the United States and Canada within the Council of Academic Family Medicine Educational Research Alliance (CERA) survey. The survey assessed loneliness symptoms in respondents' professional lives, professional relationships, organizational engagement, and demographics. Loneliness scores ranged from 3 (least lonely) to 9 (most lonely), with scores of 6 or greater indicating loneliness. RESULTS: Of 227 eligible chairs, 114 (50.2%) responded. The mean loneliness score was 4.77, with 35.8% of respondents classified as lonely. While 56.1% reported no change in loneliness since becoming chair, 29.4% reported increased feelings of loneliness. The number of trusted colleagues within the chair's institution was significantly correlated with decrease in loneliness symptoms. Chairs reporting no trusted institutional colleagues had a mean loneliness score of 7 compared to lower scores among those with one or more trusted colleagues. External professional relationships and organizational engagement were not significantly associated with loneliness. No significant differences in loneliness were found based on age, gender, or underrepresented in medicine status. CONCLUSIONS: More than one-third of family medicine chairs experience loneliness in their professional lives. Having trusted colleagues within one's institution is associated with less identified loneliness and may be a mitigating factor. This study demonstrates the importance of identifying and mitigating loneliness in family medicine chairs and other leaders in academic medicine.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".