Findings Associated With a Novel Program Designed to Support Indigenous Faculty Members of U.S. Health Professions Schools
Bibliographic record
Abstract
Purpose: Recent studies on programs designed to support Indigenous faculty are lacking. Expanding numbers of successful Indigenous health faculty could help to improve delivery of culturally appropriate/responsive health care for Indigenous people. Methods: We enrolled nine American Indian/Alaska Native (AIAN) faculty participants in 2017 and 53 in 2018 in an Indigenous Faculty Forum (IFF). We provided instruction on academic advancement, addressed unique cultural considerations, and fostered networking and ongoing career support for AIAN faculty. We used a post-session survey, including the 22-item Diversity and Engagement Survey (DES) and focus groups, to assess initial reactions to the program and a follow-up survey to assess change at 1 year. Findings: Participants in both IFF sessions were predominantly female, most often aged 35–44 and from primary care disciplines. Two faculty members rose to a higher rank during the 1-year follow-up period. Findings from the DES illustrated that Common Purpose, Equitable Reward and Recognition, Cultural Competence, Trust, a Sense of Belonging, and Appreciation of Individual Attributes increased slightly from post-session to 1 year. The greatest change was for Respect, which increased from a mean of 3.42 (SD = 0.77) to 3.76 (SD = 0.67), p = .05. Focus group findings revealed that mentoring that includes the cultural perspectives of AIAN is lacking, as is respect for these faculty from the academic community, though survey findings showed respect improved over time. Conclusions: More tailored work is needed to support AIAN in U.S. academic settings if they are to achieve academic success and become role models for others entering academic settings.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".