Remote Forensic Psychological Assessment in Civil Cases: Considerations for Experts Assessing Harms from Early Life Abuse
Bibliographic record
Abstract
The COVID-19 pandemic has brought to the fore the question of whether psycho-legal assessments can be executed remotely in a manner that adheres to the rigorous standards applied during in-person assessments. General guidelines have evolved, but to date, there are no explicit directives about whether and how to proceed. This paper reviews professional, ethical, and legal challenges that experts should consider before conducting such an evaluation remotely. Although the discussion is more widely applicable, remote forensic psychological assessment of adults alleging childhood abuse is used as an example throughout, due to the complexity of these cases, the ethical dilemmas they can present, and the need to carefully assess non-verbal trauma-related symptoms. The use of videoconferencing technology is considered in terms of potential benefits of this medium, as well as challenges this method could pose to aspects of interviewing and psychometric testing. The global pandemic is also considered with respect to its effects on functioning and mental health and the confounding impact such a crisis has on assessing the relationship between childhood abuse and current psychological functioning. Finally, for those evaluators who want to engage in remote assessment, practice considerations are discussed.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".