Beyond Googling: The Ethics of Using Patients' Electronic Footprints in Psychiatric Practice
Bibliographic record
Abstract
Electronic communications are an increasingly important part of people's lives, and much information is accessible through such means. Anecdotal clinical reports indicate that mental health professionals are beginning to use information from their patients' electronic activities in treatment and that their data-gathering practices have gone far beyond simply searching for patients online. Both academic and private sector researchers are developing mental health applications to collect patient information for clinical purposes. Professional societies and commentators have provided minimal guidance, however, about best practices for obtaining or using information from electronic communications or other online activities. This article reviews the clinical and ethical issues regarding use of patients' electronic activities, primarily focusing on situations in which patients share information with clinicians voluntarily. We discuss the potential uses of mental health patients' electronic footprints for therapeutic purposes, and consider both the potential benefits and the drawbacks and risks. Whether clinicians decide to use such information in treating any particular patient-and if so, the nature and scope of its use-requires case-by-case analysis. But it is reasonable to assume that clinicians, depending on their circumstances and goals, will encounter circumstances in which patients' electronic activities will be relevant to, and useful in, treatment.
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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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".