Exploring the Practice of Patient & Family Member Covert Video
Bibliographic record
Abstract
The proliferation of smart phones and other low cost portable electronic devices that can record audio and video has enabled individuals to conduct Covert Video Surveillance (CVS) in a variety of settings, including patient care areas. Devices that can be easily concealed means that obtaining consent or openly discussing privacy, dissatisfaction or loss of trust in the therapeutic relationship can be circumvented. Hospital staff members have expressed concerns about their own privacy interests and the privacy of other patients when a patient or family member discloses that CVS has occurred. CVS has been used in situations of suspected child abuse to record interactions between child and parent without the knowledge of the parent (Shabde & Craft 1999) and to monitor cases where parents are suspected of Münchausen syndrome by proxy or healthcare providers are under suspicion of elder abuse (Shabde & Craft 1999; Toben & Cordon 2010). The recent sexual assault conviction of Calgary psychiatrist, Dr. Aubrey Levin, was in a large part due to evidence obtained by one of his patients who wore a $240 spy watch to capture video evidence of the assault (Blackwell 2012). Absent suspected criminal abuse situations such as these, which are extremely rare, healthcare providers and organizations must consider how they will respond to the use of CVS of healthcare providers for issues such as quality of care concerns and acceptable practice. Based on our experience with CVS situations, patient and family members typically resort to CVS either based on a general concern that something
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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; both teacher heads agree on what is shown here.
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".