Memory Deficits for Health Information Provided Through a Telehealth Video Conferencing System
Bibliographic record
Abstract
It is critical to remember details about meetings with healthcare providers. Forgetting could result in inadequate knowledge about ones' health, non-adherence with treatments, and poorer health outcomes. Hearing the health care provider plays a crucial role in consolidating information for recall. The recent COVID-19 pandemic has meant a rapid transition to videoconference-based medicine, here described as telehealth. When using telehealth speech must be filtered and compressed, and research has shown that degraded speech is more challenging to remember. Here we present preliminary results from a study that compared memory for health information provided in-person to telehealth. The data collection for this study was stopped due to the pandemic, but the preliminary results are interesting because the pandemic forced a rapid transition to telehealth. To examine a potential memory deficit for health information provided through telehealth, we presented older and younger adults with instructions on how to use two medical devices. One set of instructions was presented in-person, and the other through telehealth. Participants were asked to recall the instructions immediately after the session, and again after a 1-week delay. Overall, the number of details recalled was significantly lower when instructions were provided by telehealth, both immediately after the session and after a 1-week delay. It is likely that a mix of technological and communication strategies by the healthcare provider could reduce this telehealth memory deficit. Given the rapid transition to telehealth due to COVID-19, highlighting this deficit and providing potential solutions are timely and of utmost importance.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".