Integrating Social Determinants of Health Into Clinical Formulations: The Need for a Biaxial System in the <i>DSM</i> and Psychiatry
Bibliographic record
Abstract
Although social determinants of health (SDOH) have a significant impact on health outcomes and many are already included among the “Other Conditions That May Be a Focus of Clinical Attention” in the DSM-5, general awareness of these codes and the importance of using them to communicate SDOH has not occurred. This Open Forum proposes that the DSM-5 adopt a biaxial system of assessment to enhance their consideration and reporting. A biaxial approach, the authors argue, when combined with financial incentives, will increase the likelihood of SDOH reporting and potentially improve care.
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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 | 0.092 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".