Bibliographic record
Abstract
Clinicians affiliated with medical human rights programs throughout the United States perform forensic evaluations of asylum seekers. Much of the best practice literature reflects the perspectives of clinicians and attorneys, rather than the viewpoints of immigration judges who incorporate forensic reports into their decision-making. The purpose of this study was to assess former immigration judges’ perspectives on forensic mental health evaluations of asylum seekers. We examined the factors that immigration judges use to assess the affidavits resulting from mental health evaluations and explored their attitudes toward telehealth evaluations. We conducted semistructured interviews in April and May 2020 with nine former judges and systematically analyzed them using consensual qualitative research methodology. Our findings were grouped in five domains: general preferences for affidavits; roles of affidavits in current legal climate; appraisal and comparison of sample affidavits; attitudes toward telephonic evaluations; and recommendations for telephonic evaluations. Forensic evaluators should consider the practice recommendations of judges, both for telephonic and in-person evaluations, which can bolster the usefulness of their evaluations in the adjudication process. To our knowledge, this is the first published study to incorporate immigration judges’ perceptions of forensic mental health evaluations, and the first to assess judges’ attitudes toward telephonic evaluations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".