Professional Identity, Job Satisfaction, and Commitment of Nonphysician Faculty in Academic Family Medicine
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Nonphysician faculty are common in academic family medicine departments and residencies. The objective of this study was to examine whether these nonphysician faculty have adopted a professional identity of family medicine and how that relates to job satisfaction and organizational commitment. METHODS: In 2017, a survey of nonphysician members of the Council of Academic Family Medicine organizations in the United States and Canada was conducted. The overall response rate for the survey was 52.6% (526/1,001). The current analysis was conducted on the individuals who met all of the inclusion criteria and had complete data on all investigated scales (n=360). Scales on professional identity, job satisfaction, and organizational commitment were examined along with age, gender, race, and professional characteristics. RESULTS: The respondents indicated a professional identity with family medicine, commitment to their organization, and high job satisfaction. There was a lack of association with gender for these primary variables. Professional identity had a moderately positive relationship with years in family medicine (r=0.23). Professional identity had a moderately strong positive relationship with both commitment to the organization (r=0.41), and job satisfaction (r=0.43). In multivariate regressions, race/ethnicity was associated with both professional identity (P<.05) and job satisfaction (P<.05), with nonwhites having lower professional identity and job satisfaction. CONCLUSIONS: The results of this survey of nonphysician faculty in family medicine indicated a high professional identity to family medicine, high job satisfaction, and commitment to their organization. Strategies including cultural competency training may serve as important tools to avoid dissatisfaction or turnover among this key workforce element in academic family 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".