Valuing Diversity and Inclusion in Health Care to Equip the Workforce: Survey Study and Pathway Analysis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic, with all its virus variants, remains a serious situation. Health systems across the United States are trying their best to respond. On average, the health care workforce is relatively homogenous, even though it cares for a highly diverse array of patients. This perennial problem in the US health care workforce has only been accentuated during the COVID-19 pandemic. Medical workers should reflect on the variety of patients they care for and strive to understand their mindsets within the larger contexts of culture, gender, sexual orientation, religious beliefs, and socioeconomic realities. Along with talent and skills, diversity and inclusion (D&I) are essential for maintaining a workforce that can treat the myriad needs and populations that health systems serve. Developing hiring strategies that will help achieve greater workforce diversity remains a challenge for health system leaders. OBJECTIVE: The primary aims of this study were to: (1) explore the characteristics of US health systems and their associations with D&I practices and benefits, (2) examine the associations between D&I practices and three pathways to equip workforces, and (3) examine the associations between the three pathways to better equip workforces and business and service benefits. The three pathways are: (1) improving D&I among existing employees (IMPROVE), (2) using multiple channels to find and recruit the workforce (RECRUIT), and (3) collaborating with universities to find new talent and establish plans to train students (COLLABORATE). METHODS: During February to March 2021, 625 health systems in the United States were surveyed with the help of a consultant, 135 (21.6%) of whom responded. We assessed workforce talent- and diversity-relevant factors. We collected secondary data from the Agency for Healthcare Research and Quality Compendium of the US Health Systems, leading to a matched data set of 124 health systems for analysis. We first explored differences in diversity practices and benefits across the health systems. We then examined the relationships among diversity practices, pathways, and benefits. RESULTS: Health system characteristics such as size, location, ownership, teaching, and revenue have varying associations with diversity practices and outcomes. D&I and talent strategies exhibited different associations with the three workforce pathways. Regarding the mediating effects, the IMPROVE pathway seems to be more effective than the RECRUIT and COLLABORATE pathways, enabling the diversity strategy to prompt business or service benefits. Moreover, these pathway effects go hand-in-hand with a talent strategy, indicating that both talent and diversity strategies need to be aligned to achieve the best results for a health system. CONCLUSIONS: Diversity and talent plans can be aligned to realize multiple desired benefits for health systems. However, a one-size-fits-all approach is not a viable strategy for improving D&I. Health systems need to follow a multipronged approach based on their characteristics. To get D&I right, proactive plans and genuine efforts are essential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.019 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.033 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".