Predictors of job satisfaction among academic family medicine faculty: Findings from a faculty work-life and leadership survey.
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of job satisfaction among academic family medicine faculty members. DESIGN: A comprehensive Web-based survey of all faculty members in an academic department of family medicine. Bivariate and multivariable analyses (logistic regression) were used to identify variables associated with job satisfaction. SETTING: The Department of Family and Community Medicine at the University of Toronto in Ontario and its 15 affiliated community teaching hospitals and community-based teaching practices. PARTICIPANTS: All 1029 faculty members in the Department of Family and Community Medicine were invited to complete the survey. MAIN OUTCOME MEASURES: Faculty members' demographic and practice information; teaching, clinical, administration, and research activities; leadership roles; training needs and preferences; mentorship experiences; health status; stress levels; burnout levels; and job satisfaction. Faculty members' perceptions about supports provided, recognition, communication, retention, workload, teamwork, respect, resource distribution, remuneration, and infrastructure support. Faculty members' job satisfaction, which was the main outcome variable, was obtained from the question, "Overall, how satisfied are you with your job?" RESULTS: Of the 1029 faculty members, 687 (66.8%) responded to the survey. Bivariate analyses revealed 26 predictors as being statistically significantly associated with job satisfaction, including faculty members' ratings of their local department and main practice setting, their ratings of leadership and mentorship experiences, health status variables, and demographic variables. The multivariable analyses identified the following 5 predictors of job satisfaction: the Maslach Burnout Inventory subscales of emotional exhaustion and personal accomplishment; being born in Canada; the overall quality of mentorship that was received being rated as very good or excellent; and teamwork being rated as very good or excellent. CONCLUSION: The findings from this study show that job satisfaction among academic family medicine faculty members is a multi-dimensional construct. Future improvement in overall level of job satisfaction will therefore require multiple strategies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".