Embodying digital spaces in a clinical encounter
Bibliographic record
Abstract
What is it like to interact in a clinical setting when a technological device is participating? This inquiry was conducted in a primary healthcare setting, with the aim of shedding light on clinicians’ and patients’ experiences regarding the use of a tablet-with-app, intended for a more systematic assessment, as well as electronic registration and storing of patient data. In this paper, we present an account of four experiential exemplars of adopting an eTool in a clinical setting. The “faciality” of the digital device seems to be important to both patients and clinicians, as well as the interaction between them. The “face” can be used for engaging in conversation, addressing awkward topics, communicating, or inviting involvement. The face can also be used for just resting the eyes or lowering the gaze to maintain a low profile during the clinical encounter. Concurrently, the size, the shape, and the backside of the eTool’s face can mediate distance. We expand the notion of “screen sharing” and suggest that humans’ ability to move from one mode to another and embody digital spaces in the clinical encounter seems enhanced by their ability to include the eTool’s face in their interaction. This knowledge can be used in the development of digital tools for teaching, as well as for health professions.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".