Individuals recording clinical encounters
Bibliographic record
Abstract
BACKGROUND: Clinicians and their employers, concerned with privacy and liability, are often hesitant to support the recording of clinical encounters. However, many people wish to record encounters with healthcare professionals. It is therefore important to understand how existing law applies to situations where an individual requests to record a clinical encounter. METHODS: We searched for and reviewed relevant legal documents that could apply to recording clinical encounters. We limited the scope by purposefully examining relevant law in nine countries: Australia, Brazil, Canada, France, Germany, India, Mexico, the United Kingdom and the United States. We analyzed legal texts for consents needed to record a conversation, whether laws applied to remote or face-to-face conversations and penalties for violations. FINDINGS: Most jurisdictions have case law or statutes, derived from a constitutional right to privacy, or a wiretapping or eavesdropping statute, governing the recording of private conversations. However, little to no guidance exists on how to translate constitutional principles and case law into advice for people seeking to record their medical encounters. INTERPRETATION: The law has not kept pace with people's wish to record clinical interactions, which has been enabled by the arrival of mobile technology.
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 | 0.026 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".