Public Health Workforce Perceptions About Organizational Commitment to Diversity, Equity, and Inclusion: Results From PH WINS 2021
Bibliographic record
Abstract
OBJECTIVE: In response to calls to achieve racial equity, racism has been declared as a public health crisis. Diversity, equity, and inclusion (DEI) is an approach public health organizations are pursuing to address racial inequities in health. However, public health workforce perceptions about organizational commitment to DEI have not yet been assessed. Using a nationally representative survey of public health practitioners, we examine how perceptions about supervisors' and managers' commitment to DEI and their ability to support a diverse workforce relate to perceptions of organizational culture around DEI. METHODS: Data from the 2021 Public Health Workforce Interests and Needs Survey (PH WINS) to examine the relationship between public health employees' perceptions about their organization's commitment to DEI and factors related to those perceptions. PH WINS received 44 732 responses (35% response rate). We calculated descriptive statistics and constructed a logistic regression model to assess these relationships. RESULTS: Findings show that most public health employees perceive that their organizations are committed to DEI; however, perceptions about commitment to DEI vary by race, ethnicity, gender identity, and organizational setting. Across all settings, White respondents were more likely to agree with the statement, "My organization prioritizes diversity, equity, and inclusion" (range, 70%-75%), than Black/African American (range, 55%-65%) and Hispanic/Latino respondents (range, 62.5%-72.5%). Perception that supervisors worked well with individuals with diverse backgrounds had an adjusted odds ratio (AOR) of 5.37 ( P < .001); organizational satisfaction had an AOR of 4.45 ( P < .001). Compared with White staff, all other racial and ethnic groups had lower AOR of reporting their organizations prioritized DEI, with Black/African American staff being the lowest (AOR = 0.55), followed by Hispanic/Latino staff (AOR = 0.71) and all other staff (AOR = 0.82). CONCLUSIONS: These differences suggest that there are opportunities for organizational DEI commitment to marginalized public health staff to further support DEI and racial equity efforts. Building a diverse public health workforce pipeline will not be sufficient to achieve health equity if staff perceive that their organization does not prioritize DEI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.032 |
| Research integrity | 0.000 | 0.002 |
| 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 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".