Impact of microgeography on communication dynamics in a healthcare environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: For growing healthcare organisations, anchored resources-assets that are not easily movable-may complicate expansion and distort workflow patterns. We examine work patterns at a radiation oncology department of a major Canadian hospital. As this department doubled its size, healthcare providers remained bound to treatment planning rooms and radiation machines at the original site. This study examines workplace communication and interactions before and after the expansion. METHODS: We conducted regression analyses using a unique dataset merging email communications, badge swipes, office locations and organisation charts for individuals that routinely use the treatment planning room (n=232). We use a difference-in-differences framework to compare individuals' behaviours before and after the expansion. Our dependent variables were how often individuals accessed the treatment planning room and email volumes between two individuals. FINDINGS: We find an overall decrease in the use of the treatment planning room, though the effect was larger for those that moved away from it. Further, we find an increase in email communication for dyads of individuals separated in the move, but only if they belonged to different departments. PRACTICAL IMPLICATIONS: Our research points to complex interdependencies among healthcare providers, shedding light on how hospital expansion may have unintended consequences. Healthcare leaders should acknowledge that interaction patterns will be affected when healthcare providers are separated from each other or from anchored resources. Shifting to remote interactions may be adequate in some instances; in others, it may negatively affect work outcomes as well as the engagement and satisfaction of providers and patients.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it