“You Need ID to Get ID”: A Scoping Review of Personal Identification as a Barrier to and Facilitator of the Social Determinants of Health in North America
Bibliographic record
Abstract
Personal identification (PID) is an important, if often overlooked, barrier to accessing the social determinants of health for many marginalized people in society. A scoping review was undertaken to explore the range of research addressing the role of PID in the social determinants of health in North America, barriers to acquiring and maintaining PID, and to identify gaps in the existing research. A systematic search of academic and gray literature was performed, and a thematic analysis of the included studies (n = 31) was conducted. The themes identified were: (1) gaining and retaining identification, (2) access to health and social services, and (3) facilitating identification programs. The findings suggest a paucity of research on PID services and the role of PID in the social determinants of health. We contend that research is urgently required to build a more robust understanding of existing PID service models, particularly in rural contexts, as well as on barriers to accessing and maintaining PID, especially among the most marginalized groups in society.
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 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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".