Bibliographic record
Abstract
PURPOSE: Social determinants of health (SDOH), the environmental, economic, and social factors that influence people's health outcomes, are widely recognized across health and human services. In addition, there are other factors that can exacerbate SDOH; among them is immigration status. Its influence is so profound that it has been suggested that immigration be considered an SDOH in and of itself (National Academies of Sciences, Engineering, and Medicine, 2018). Across the continuum, case managers need to be aware of the immigration status of their clients (the individuals for whom they advocate and provide services). This is particularly important when addressing the care needs and discharge plans for clients in acute care, community-based health, and workers' compensation. With workers' compensation, when an individual is undocumented and severely injured, immigration status directly impacts the services they may receive under state mandates. Moreover, such limitations can present ethical dilemmas for case managers, including what happens to workers if they are returned to their home countries. PRIMARY PRACTICE SETTINGS: SDOH and immigration status can impact individuals in acute care, subacute care, community-based care, and workers' compensation. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: SDOH and immigration status highlight the disparities that exist within health and human services. Although equity is a core value of case management practice, the case manager's ability to provide equal access to care and resources can be severely limited because of the individual's immigration status. At all times, case managers must practice within their licensure and certifications. By recognizing that immigration status should be an SDOH, case management professionals and health and human services organizations can elevate the discussion of how to care for individuals with catastrophic injuries and illnesses who are undocumented.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".