Real time location systems: Is it big brother or a big opportunity for professional practice?
Bibliographic record
Abstract
Purpose: Real Time Location Systems (RTLS) is an emerging health care technology with the potential to capture data that can be used to improve professional practice and patient outcomes. However, there is a paucity of literature in this area to guide health professionals and leaders in both the implementation and use of RTLS data. To address this gap in the literature, this qualitative study was designed to explore how staff perceive and experience RTLS, and how health care providers anticipate using RTLS data for professional practice and clinical decision making.Results: Interviews and focus groups were conducted with 31 health care professionals who work in a community hospital in Canada. There was variation between the participants in terms of the experience of being monitored, the intensity of emotions related to RTLS and being monitored, the degree to which RTLS influenced clinical decision making and reflection, and the perceptions of usefulness of RTLS data for professional practice. Three key themes emerged from the data: (1) the experience of being monitored, (2) anticipating using the data and (3) claiming the data for professional practice.Conclusions: Supports are vital to the successful adoption of RTLS and to enable health care professionals to claim and use RTLS data for professional practice and clinical decision making. During the implementation and use of RTLS data, it is crucial to recognize that RTLS data only represent the time spent in a location, and not the professional or knowledge-based practice of health professionals. Further research is required to understand the leadership strategies to guide the use of RTLS data.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".