Breaking Barriers: Retention Strategies for Underrepresented Groups in the IT Workforce
Bibliographic record
Abstract
The underrepresentation and high attrition rates of minorities in the IT workforce remain critical challenges despite diversity initiatives. Systemic barriers, including workplace biases, limited mentorship opportunities, and rigid workplace structures, hinder career advancement and drive turnover. This scoping review examines these challenges and identifies retention strategies for underrepresented IT professionals. Using the PRISMA framework, 1,241 articles were screened, with 30 peer-reviewed studies included. Six core strategies were identified: mentorship and sponsorship programs, employee resource groups (ERGs), flexible work policies, workplace accommodations, inclusive hiring practices, and diversity, equity, and inclusion (DEI) training. However, these strategies face challenges such as inconsistent implementation, limited access to informal networks, and lack of structural reform. These preliminary findings provide a foundation for future research, including stakeholder consultations and cross-sector comparisons, to validate and refine strategies. This in-progress study underscores the need for targeted approaches to foster a more inclusive IT workforce.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".