EXPLORING BED SENSOR SLEEP TECH: INSIGHTS FROM INTERDISCIPLINARY TEAM IN A GERIATRIC ASSESSMENT INPATIENT SETTING
Bibliographic record
Abstract
Abstract Poor sleep can affect cognitive function and decrease overall quality of life. Evaluating sleep is critical in the care of hospitalized older adults. This study compared the use of traditional paper-based logs charted by nurses to record sleep patterns with smart sensors offering continuous monitoring and analysis of sleep patterns. The goal was to investigate the perspectives of staff members in an interdisciplinary team on sleep monitoring methods. We also asked participants about barriers and facilitators in implementing bed sensor technology in an Inpatient setting. Employing Interpretive Descriptive methodology, we conducted interviews and focus groups involving 29 staff members with diverse roles in one inpatient setting. The Consolidated Framework for Implementation Research (CFIR) guided our data analysis. Our findings indicate that while the traditional paper-based method, somnolog, is prone to inaccuracies from subjective estimates and sleep disturbances, bed sensor sleep technology is perceived as efficient, data-driven, and evidence-supported. Barriers to technology adoption include resistance to change, consent issues, patient comfort and safety concerns, and familiarity with the technology. On the other hand, facilitating factors comprise orientation and training, trial integration, effective communication, and evidence-based incentives. Utilizing the CFIR framework provides valuable insights into the challenges and aids in incorporating technology into care settings. Future research should focus on practical strategies involving interdisciplinary teams to facilitate innovative practices.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".