Belonging in Hospital Medicine: Insights From Mapping Hospitalists’ Priorities for Inclusive Workplace Strategies
Bibliographic record
Abstract
Introduction We aimed to identify priority areas among strategies to foster an inclusive and engaging work environment and to examine how these strategies relate to one another through a needs assessment in a large hospital medicine group. Methods We conducted a secondary analysis of an anonymous survey administered in February 2023 at the University of Kentucky, a university hospital in the southern United States. A total of 85 respondents at the Division of Hospital Medicine ranked nine key strategies by urgency, including recruitment, retention, educational opportunities, belonging, psychological safety, inclusive workplace, inclusive policies, equity in care, and opportunities for collaboration. We used unfolding multidimensional scaling (UMDS) to visualize the relationships among respondents and strategies. Results Of the 85 complete responses, the respondents were primarily physicians (60 (70%)), White (51 (60%)), and women (43 (51%)). Retention (3.6±2.6) and belongingness (4.1±2.6) had the lowest (i.e., highest priority) mean ranks. The UMDS plot suggested one dimension spanning from diversity to inclusivity and the other from organizational to interpersonal continuum. Belonging and psychological safety clustered in the interpersonal-inclusivity domain, while recruitment, retention, and collaboration clustered in the interpersonal-diversity domain. Inclusive workplace, inclusive policies, and equity in care were aligned within the organizational-inclusivity domain. Education was an outlier, suggesting varied interpretations of its importance. Conclusion Belonging emerged as a high-priority strategy closely linked with psychological safety, suggesting its role in workforce inclusivity and engagement. Fostering belonging may support retention and promote a more inclusive culture in academic hospital medicine. Clarifying the definition and measurement of belonging can enhance its integration into institutional 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".